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Known as a bio-limiting metal, high abundances of iron in sea water can amplify biological productivity. The growth of diatoms and other photosynthetic organisms increases, providing more food for grazing organisms like foraminifera. The net result is more organic matter in surface waters and ultimately in surface sediments. Existing satellite data show increases in ocean chlorophyll in areas affected by volcanic eruptions. We infer from this that iron derived from volcanic ash does increase biological productivity. However, the relative increase in productivity is unknown. We examined 3 sediment cores from the Equatorial Western Pacific to analyze the relationship between volcanic ash and biological productivity: RC14-44, RC14-66, and RC14-67. All contain black or dark-colored foraminifera within ash layers and white-shelled foraminifera outside ash layers. We attribute the dark material outside and inside the foraminifera to organic carbon and metals. In our cores, some foraminifera are covered in iron sulfide (FeS), which could be pyrite, and contain large amounts of carbon as well as high abundances of aluminum and silicon. We examined barium concentrations to gain further knowledge of biological productivity at specific core depths as barium is a marker for primary productivity. We found that barium levels within ash layers increased at least ten-fold. Within ash layers, we also noticed that the ashes with higher amounts of fine silt and clay sized material have the greatest increase in barium content, perhaps related to explosion size. This pattern of increases in Ba, metals and organic carbon within ash layers compared to surrounding sediments shows that volcanic ash deposition increases marine productivity. For future research, measuring markers for biological productivity like biogenic silica content and loss on ignition (LOI) within and outside ash layers would further clarify the relationship between volcanic ash deposition and biological productivity. 

Volcanic eruptions deposit Fe-bearing volcanic ash in the ocean, thereby increasing biological productivity. The increased organic matter in areas of high biological productivity uses up oxygen as this organic matter decays and sinks through the water column. Past living beings, like foraminifera, ate organic matter that was carbon-rich and sometimes had metals absorbed into their carbon, creating coatings inside and outside their shells. These coatings can tell us about how biological productivity was affected before, during, and after the volcanic eruption. The studied cores are from the northwest Pacific Ocean and are close to geologically young volcanoes that are not well understood. The two cores that we focused on were VM28-309 and VM36-15 both taken by the Vema research ship. We studied the relationship between ash deposition and biological productivity by looking at all the ash layers in both cores. We found that in most of the ash layers, there were black or dark-colored foraminifera with coatings inside and outside the shells that were often carbon-rich and sometimes metal-rich. We attribute this coating to the increase of organic matter in surface waters when there was deposition of large amounts of volcanic ash. We also found high concentrations of Barium metal in VM28-309. Barium (Ba) is a biological marker because most or all Ba originates from the organic matter contained in sediments. We found that ash layers containing the finest materials (<38 micrometers in size) had the highest Ba content. For accurate results, we must sample above and below ash layers and select more sediment cores in the area. Also, Barium corrections must be done using data on biogenic silica contents. Loss on ignition (LOI) data will give us an estimate of the total organic carbon in each sample- allowing a second direct assessment of the increase in biological productivity produced by the deposition of volcanic ash. 

The COVID-19 pandemic has mainstreamed human mobility data into the public domain, with research focused on understanding the impact of mobility reduction policies as well as on regional COVID-19 case prediction models. Nevertheless, current research on COVID-19 case prediction tends to focus on performance improvements, masking relevant insights about when mobility data does not help, and more importantly, why, so that it can adequately inform local decision making. In this article, we carry out a systematic analysis to reveal the conditions under which human mobility data provides (or not) an enhancement over individual regional COVID-19 case prediction models that do not use mobility as a source of information. Our analysis— focused on U.S. county-based COVID-19 case prediction models—shows that (1) at most, 60% of counties improve their performance after adding mobility data; (2) the performance improvements are modest, with median correlation improvements of approximately 0.13; (3) improvements were lower for counties with higher Black, Hispanic, and other non-White populations as well as low-income and rural populations, pointing to potential bias in the mobility data negatively impacting predictive performance; and (4) different mobility datasets, predictive models, and training approaches bring about diverse performance improvements. 

Some satellite data show an increase in ocean chlorophyll in areas affected by volcanic eruptions. These increases in ocean color are thought to reflect an increase in photosynthetic activity by phytoplankton. These increases in primary production have been attributed to iron (Fe) from volcanic ash, particularly in high-latitude regions where primary productivity is limited by low Fe (the iron fertilization hypothesis). However, photosynthesis also appears to increase in the tropical ocean, for example in the Sunda and Ryukyu arcs and the Bismarck Sea, areas usually not thought to be iron limited. To examine the effects of volcanic ejecta on productivity in other areas, we examine relationships between ash deposition and biological productivity in three cores, RC14-44 (Sunda arc), VM28-309 (Ryukyu arc) and VM33-116 (Bismarck Sea). These cores contain volcanic ash layers with black or dark-colored foraminifera, different from the bright white foraminifera found outside of the ash layers. This dark coloration results primarily from organic carbon. In RC14-44, some foraminifera are coated with FeS and also contain high amounts of internal carbon. In VM28-309 and VM33-116, some foraminifera are filled with organic carbon rich materials, or have coatings rich in carbon. Occasionally, there are local enrichments in Fe within the foraminifera, indicative of extensive redox cycling. We attribute this carbon to increased biological productivity in these intervals. Barium (Ba) concentrations, a proxy for primary productivity because most or all Ba originates from organic matter contained in the sediment, is also enriched by up to 30-fold in the sediments containing ash. The ash layers with the highest amounts of fine material exhibit the largest enrichments in Ba, suggesting ash texture may influence the resulting changes in marine productivity. Overall, we find clear evidence that ash depositions increase both primary production and carbon export to sediments. Loss on ignition (LOI) and biogenic silica contents between and within ash layers, are potentially useful to further examine both the coupling between production and carbon burial, and the influence of ash deposition on phytoplankton community structure. 

Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. Root-n consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality. We implement an automatic debiasing procedure based on automatically learning the Riesz representation of the linear functional using Neural Nets and Random Forests. Our method only relies on black-box evaluation oracle access to the linear functional and does not require knowledge of its analytic form. We propose a multitasking Neural Net debiasing method with stochastic gradient descent minimization of a combined Riesz representer and regression loss, while sharing representation layers for the two functions. We also propose a Random Forest method which learns a locally linear representation of the Riesz function. Even though our method applies to arbitrary functionals, we experimentally find that it performs well compared to the state of art neural net based algorithm of Shi et al. (2019) for the case of the average treatment effect functional. We also evaluate our method on the problem of estimating average marginal effects with continuous treatments, using semi-synthetic data of gasoline price changes on gasoline demand. Code available at github.com/victor5as/RieszLearning. 

Abstract The Gravitational-Wave Transient Catalog (GWTC) is a collection of short-duration (transient) gravitational-wave signals identified by the LIGO–Virgo–KAGRA Collaboration in gravitational-wave data produced by the eponymous detectors. The catalog provides information about the identified candidates, such as the arrival time and amplitude of the signal and properties of the signal’s source as inferred from the observational data. GWTC is the data release of this dataset, and version 4.0 extends the catalog to include observations made during the first part of the fourth LIGO–Virgo–KAGRA observing run up until 2024 January 31. This Letter marks an introduction to a collection of articles related to this version of the catalog, GWTC-4.0. The collection of articles accompanying the catalog provides documentation of the methods used to analyze the data, summaries of the catalog of events, observational measurements drawn from the population, and detailed discussions of selected candidates. 

Abstract We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO–Virgo–KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, nonnegligible spin–orbit misalignment, and unequal mass ratios between their constituent black holes. These properties are characteristic of binaries in which the more massive object was itself formed from a previous binary black hole merger and suggest that the sources of GW241011 and GW241110 may have formed in dense stellar environments in which repeated mergers can take place. As the third-loudest gravitational-wave event published to date, with a median network signal-to-noise ratio of 36.0, GW241011 furthermore yields stringent constraints on the Kerr nature of black holes, the multipolar structure of gravitational-wave generation, and the existence of ultralight bosons within the mass range 10⁻¹³–10⁻¹²eV. 

Abstract On 2023 November 23, the two LIGO observatories both detected GW231123, a gravitational-wave signal consistent with the merger of two black holes with masses $137_{-18}^{+23}{M}_{\odot }$and $101_{-50}^{+22}{M}_{\odot }$(90% credible intervals), at a luminosity distance of 0.7–4.1 Gpc, a redshift of $0.40_{-0.25}^{+0.27}$, and with a network signal-to-noise ratio of ∼20.7. Both black holes exhibit high spins— $0.90_{-0.19}^{+0.10}$and $0.80_{-0.52}^{+0.20}$, respectively. A massive black hole remnant is supported by an independent ringdown analysis. Some properties of GW231123 are subject to large systematic uncertainties, as indicated by differences in the inferred parameters between signal models. The primary black hole lies within or above the theorized mass gap where black holes between 60–130M_⊙should be rare, due to pair-instability mechanisms, while the secondary spans the gap. The observation of GW231123 therefore suggests the formation of black holes from channels beyond standard stellar collapse and that intermediate-mass black holes of mass ∼200M_⊙form through gravitational-wave-driven mergers.