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1. Redesigning the Trading Zone between Systematics and Conservation: Insights from Malagasy mouse lemur classifications, 1982 to present

   https://doi.org/10.3897/biss.4.59234  

   Franz, Nico ; Sterner, Beckett ; Upham, Nathan ; Cortés Hernández, Kevin ( October 2020 , Biodiversity Information Science and Standards)

   Translating information between the domains of systematics and conservation requires novel information management designs. Such designs should improve interactions across the trading zone between the domains, herein understood as the model according to which knowledge and uncertainty are productively translated in both directions (cf. Collins et al. 2019). Two commonly held attitudes stand in the way of designing a well-functioning systematics-to-conservation trading zone. On one side, there are calls to unify the knowledge signal produced by systematics, underpinned by the argument that such unification is a necessary precondition for conservation policy to be reliably expressed and enacted (e.g., Garnett et al. 2020). As a matter of legal scholarship, the argument for systematic unity by legislative necessity is principally false (Weiss 2003, MacNeil 2009, Chromá 2011), but perhaps effective enough as a strategy to win over audiences unsure about robust law-making practices in light of variable and uncertain knowledge. On the other side, there is an attitude that conservation cannot ever restrict the academic freedom of systematics as a scientific discipline (e.g., Raposo et al. 2017). This otherwise sound argument misses the mark in the context of designing a productive trading zone with conservation. The central interactional challenge is not whether the systematic knowledge can vary at a given time and/or evolve over time, but whether these signal dynamics are tractable in ways that actors can translate into robust maxims for conservation. Redesigning the trading zone should rest on the (historically validated) projection that systematics will continue to attract generations of inspired, productive researchers and broad-based societal support, frequently leading to protracted conflicts and dramatic shifts in how practioners in the field organize and identify organismal lineages subject to conservation. This confident outlook for systematics' future, in turn, should refocus the challenge of designing the trading zone as one of building better information services to model the concurrent conflicts and longer-term evolution of systematic knowledge. It would seem unreasonable to expect the International Union for Conservation of Nature (IUCN) Red List Index to develop better data science models for the dynamics of systematic knowledge (cf. Hoffmann et al. 2011) than are operational in the most reputable information systems designed and used by domain experts (Burgin et al. 2018). The reasonable challenge from conservation to systematics is not to stop being a science but to be a better data science. In this paper, we will review advances in biodiversity data science in relation to representing and reasoning over changes in systematic knowledge with computational logic, i.e., modeling systematic intelligence (Franz et al. 2016). We stress-test this approach with a use case where rapid systematic signal change and high stakes for conservation action intersect, i.e., the Malagasy mouse lemurs ( Microcebus É. Geoffroy, 1834 sec. Schüßler et al. 2020), where the number of recognized species-level concepts has risen from 2 to 25 in the span of 38 years (1982–2020). As much as scientifically defensible, we extend our modeling approach to the level of individual published occurrence records, where the inability to do so sometimes reflects substandard practice but more importantly reveals systemic inadequacies in biodiversity data science or informational modeling. In the absence of shared, sound theoretical foundations to assess taxonomic congruence or incongruence across treatments, and in the absence of biodiversity data platforms capable of propagating logic-enabled, scalable occurrence-to-concept identification events to produce alternative and succeeding distribution maps, there is no robust way to provide a knowledge signal from systematics to conservation that is both consistent in its syntax and acccurate in its semantics, in the sense of accurately reflecting the variation and uncertainty that exists across multiple systematic perspectives. Translating this diagnosis into new designs for the trading zone is only one "half" of the solution, i.e., a technical advancement that then would need to be socially endorsed and incentivized by systematic and conservation communities motivated to elevate their collaborative interactions and trade robustly in inherently variable and uncertain information. 

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2. Search for point sources of ultra-high-energy photons with the Telescope Array surface detector

   https://doi.org/10.1093/mnras/stz3618  

   Abbasi, R.U. ( February 2020 , Monthly notices of the Royal Astronomical Society)

   Ultra-high-energy (UHE) photons are an important tool for studying the high-energy Universe. A plausible source of photons with exa-eV (EeV) energy is provided by UHE cosmic rays (UHECRs) undergoing the Greisen–Zatsepin–Kuzmin process (Greisen 1966; Zatsepin & Kuzmin 1966) or pair production process (Blumenthal 1970) on a cosmic background radiation. In this context, the EeV photons can be a probe of both UHECR mass composition and the distribution of their sources (Gelmini, Kalashev & Semikoz 2008; Hooper, Taylor & Sarkar 2011). At the same time, the possible flux of photons produced by UHE protons in the vicinity of their sources by pion photoproduction or inelastic nuclear collisions would be noticeable only for relatively near sources, as the attenuation length of UHE photons is smaller than that of UHE protons; see, for example, Bhattacharjee & Sigl (2000) for a review. There also exists a class of so-called top-down models of UHECR generation that efficiently produce the UHE photons, for instance by the decay of heavy dark-matter particles (Berezinsky, Kachelriess & Vilenkin 1997; Kuzmin & Rubakov 1998) or by the radiation from cosmic strings (Berezinsky, Blasi & Vilenkin 1998). The search for the UHE photons was shown to be the most sensitive method of indirect detection of heavy dark matter (Kalashev & Kuznetsov 2016, 2017; Kuznetsov 2017; Kachelriess, Kalashev & Kuznetsov 2018; Alcantara, Anchordoqui & Soriano 2019). Another fundamental physics scenario that could be tested with UHE photons (Fairbairn, Rashba & Troitsky 2011) is the photon mixing with axion-like particles (Raffelt & Stodolsky 1988), which could be responsible for the correlation of UHECR events with BL Lac type objects observed by the High Resolution Fly’s Eye (HiRes) experiment (Gorbunov et al. 2004; Abbasi et al. 2006). In most of these scenarios, a clustering of photon arrival directions, rather than diffuse distribution, is expected, so point-source searches can be a suitable test for photon - axion-like particle mixing models. Finally, UHE photons could also be used as a probe for the models of Lorentz-invariance violation (Coleman & Glashow 1999; Galaverni & Sigl 2008; Maccione, Liberati & Sigl 2010; Rubtsov, Satunin & Sibiryakov 2012, 2014). The Telescope Array (TA; Tokuno et al. 2012; Abu-Zayyad et al. 2013c) is the largest cosmic ray experiment in the Northern Hemisphere. It is located at 39.3° N, 112.9° W in Utah, USA. The observatory includes a surface detector array (SD) and 38 fluorescence telescopes grouped into three stations. The SD consists of 507 stations that contain plastic scintillators, each with an area of 3 m2 (SD stations). The stations are placed in the square grid with 1.2 km spacing and cover an area of ∼700 km2. The TA SD is capable of detecting extensive air showers (EASs) in the atmosphere caused by cosmic particles of EeV and higher energies. The TA SD has been operating since 2008 May. A hadron-induced EAS significantly differs from an EAS induced by a photon because the depth of the shower maximum Xmax for a photon shower is larger, and a photon shower contains fewer muons and has a more curved front (see Risse & Homola 2007 for a review). The TA SD stations are sensitive to both muon and electromagnetic components of the shower and therefore can be triggered by both hadron-induced and photon-induced EAS events. In the present study, we use 9 yr of TA SD data for a blind search for point sources of UHE photons. We utilize the statistics of the SD data, which benefit from a high duty cycle. The full Monte Carlo (MC) simulation of proton-induced and photon-induced EAS events allows us to perform the photon search up to the highest accessible energies, E ≳ 1020 eV. As the main tool for the present photon search, we use a multivariate analysis based on a number of SD parameters that make it possible to distinguish between photon and hadron primaries. While searches for diffuse UHE photons were performed by several EAS experiments, including Haverah Park (Ave et al. 2000), AGASA (Shinozaki et al. 2002; Risse et al. 2005), Yakutsk (Rubtsov et al. 2006; Glushkov et al. 2007, 2010), Pierre Auger (Abraham et al. 2007, 2008a; Bleve 2016; Aab et al. 2017c) and TA (Abu-Zayyad et al. 2013b; Abbasi et al. 2019a), the search for point sources of UHE photons has been done only by the Pierre Auger Observatory (Aab et al. 2014, 2017a). The latter searches were based on hybrid data and were limited to the 1017.3 < E < 1018.5 eV energy range. In the present paper, we use the TA SD data alone. We perform the searches in five energy ranges: E > 1018, E > 1018.5, E > 1019, E > 1019.5 and E > 1020 eV. We find no significant evidence of photon point sources in all energy ranges and we set the point-source flux upper limits from each direction in the TA field of view (FOV). The search for unspecified neutral particles was also previously performed by the TA (Abbasi et al. 2015). The limit on the point-source flux of neutral particles obtained in that work is close to the present photon point-source flux limits. 

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3. Impact of COVID Transition to Remote Learning on Engineering Self-Efficacy and Outcome Expectations

   Milord, Johanna ; Yu, Fan ; Orton, Sarah ; Flores, Lisa ; Marra, Rose ( July 2021 , 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   The outbreak of COVID-19 and sudden transition to remote learning brought many changes and challenges to higher education campuses across the nation. This paper evaluates the impact of the transition to remote learning on the engineering-related social cognitions of self-efficacy (belief in one’s abilities to successfully accomplish tasks in engineering) and outcome expectations (beliefs about the consequences of performing engineering behaviors). These social cognitions can be attributed to important academic and career outcomes, such as the development of STEM interests and goals (Lent et al., 2019) and may be especially important in the success of women in non-traditional fields such as engineering. As an extension to a NSF RIEF (Research Initiation in Engineering Formation) study evaluating engineering social cognitions, students in 8 engineering classes were surveyed at the beginning of Spring 2020 semester (N=224), shortly after the transition to remote learning (N = 190), and at the end of the semester (N=101). The classes surveyed included a common early engineering class at the sophomore level (Engineering Statics) and required junior level courses in different departments. The students were surveyed using reliable and validated instruments to measure engineering self-efficacy (Lent et al. 2005, Frantz et al. 2011), engineering outcome expectations (Lent et al. 2003, Lee et al. 2018), and engineering persistence intentions (Lent et al. 2003). The results show a gradual increase in the mean scores on the engineering self-efficacy and outcome expectation measures through the semester. Two tailed t-tests of matched participants showed no significance when comparing the data between the beginning and mid-semester surveys, as well as the mid-semester and end surveys. However, significance was found in the two engineering self-efficacy measures between the beginning and end of semester surveys. Results are compared across courses at different levels and across gender. Results indicate that despite the sudden change in instructional mode, students’ perceptions of engineering self-efficacy and outcome expectations showed a slight increase or no change. 

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4. Impact of Video-Based Analysis-of-Practice Professional Learning: Comparing Online and Face-to-face Models

   Kowalski, S. M. ; Belcastro, A. ; Hvidsten, C. ; Ollison, G. ; Askinas, K. ; De Vaul, R. ; Roehrig, G. ( September 2022 , Annual Meeting for the Society for Research on Educational Effectiveness)

   Video-based analysis of practice models have gained prominence in mathematics and science teacher education inservice professional learning. There is a growing body of evidence that these intensive professional learning (PL) models lead to positive impacts on teacher knowledge, classroom instructional practice, and student learning (Roth et al., 2018; Taylor et al., 2017), but they are expensive and difficult to sustain. An online version would have several benefits, allowing for greater reach to teachers and students across the country, but if online models were substantially less effective, then lower impacts would undercut the benefits of greater accessibility. We designed and studied a fully online version of the face-to-face Science Teachers Learning from Lesson Analysis (STeLLA) PL model (Roth, et al., 2011; Roth et al., 2018; Taylor et al., 2017). We conducted a quasi-experimental study comparing online STeLLA to face-to-face STeLLA. Although we found no significant difference in elementary student learning between the online and face-to-face versions ( p = .09), the effect size raises questions. Exploratory analyses suggest that the impact of online STeLLA on students is greater than the impact of a similar number of hours of traditional, face-to-face content deepening PL, but less than the impact of the full face-to-face STeLLA program. Differences in student populations, with higher percentages of students from racial and ethnic groups underserved by schools in the online STeLLA program, along with testing of the online STeLLA model during the pandemic, complicates interpretation of the findings. 

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5. Avenues into Integration: Communicating taxonomic intelligence from sender to recipient

   https://doi.org/10.3897/biss.4.59006  

   Sterner, Beckett ; Upham, Nathan ; Sen, Atriya ; Franz, Nico ( October 2020 , Biodiversity Information Science and Standards)

   null (Ed.)

   “What is crucial for your ability to communicate with me… pivots on the recipient’s capacity to interpret—to make good inferential sense of the meanings that the declarer is able to send” (Rescher 2000, p148). Conventional approaches to reconciling taxonomic information in biodiversity databases have been based on string matching for unique taxonomic name combinations (Kindt 2020, Norman et al. 2020). However, in their original context, these names pertain to specific usages or taxonomic concepts, which can subsequently vary for the same name as applied by different authors. Name-based synonym matching is a helpful first step (Guala 2016, Correia et al. 2018), but may still leave considerable ambiguity regarding proper usage (Fig. 1). Therefore, developing "taxonomic intelligence" is the bioinformatic challenge to adequately represent, and subsequently propagate, this complex name/usage interaction across trusted biodiversity data networks. How do we ensure that senders and recipients of biodiversity data not only can share messages but do so with “good inferential sense” of their respective meanings? Key obstacles have involved dealing with the complexity of taxonomic name/usage modifications through time, both in terms of accounting for and digitally representing the long histories of taxonomic change in most lineages. An important critique of proposals to use name-to-usage relationships for data aggregation has been the difficulty of scaling them up to reach comprehensive coverage, in contrast to name-based global taxonomic hierarchies (Bisby 2011). The Linnaean system of nomenclature has some unfortunate design limitations in this regard, in that taxonomic names are not unique identifiers, their meanings may change over time, and the names as a string of characters do not encode their proper usage, i.e., the name “Genus species” does not specify a source defining how to use the name correctly (Remsen 2016, Sterner and Franz 2017). In practice, many people provide taxonomic names in their datasets or publications but not a source specifying a usage. The information needed to map the relationships between names and usages in taxonomic monographs or revisions is typically not presented it in a machine-readable format. New approaches are making progress on these obstacles. Theoretical advances in the representation of taxonomic intelligence have made it increasingly possible to implement efficient querying and reasoning methods on name-usage relationships (Chen et al. 2014, Chawuthai et al. 2016, Franz et al. 2015). Perhaps most importantly, growing efforts to produce name-usage mappings on a medium scale by data providers and taxonomic authorities suggest an all-or-nothing approach is not required. Multiple high-profile biodiversity databases have implemented internal tools for explicitly tracking conflicting or dynamic taxonomic classifications, including eBird using concept relationships from AviBase (Lepage et al. 2014); NatureServe in its Biotics database; iNaturalist using its taxon framework (Loarie 2020); and the UNITE database for fungi (Nilsson et al. 2019). Other ongoing projects incorporating taxonomic intelligence include the Flora of Alaska (Flora of Alaska 2020), the Mammal Diversity Database (Mammal Diversity Database 2020) and PollardBase for butterfly population monitoring (Campbell et al. 2020). 

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