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Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(s) to represent very specific, precise, but, perhaps, different concepts. Under the best circumstances, misunderstanding key terms will lead toward a breakdown of efficiency. Under less optimal conditions, miscommunication will sow frustration, lead to errors, and inhibit scientific breakthroughs. Here, our research group of geoscientists and machine learning experts presents a process to help geoscientists understand the fundamentals of supervised learning by describing the general workflow (i.e., a conceptual pipeline) for supervised learning that must be understood by all the parties involved in a geoscience-machine learning endeavor. Terms critical for machine learning are introduced, defined, and used within the context of an overly simplified mock hydrological study to illustrate their appropriate usage, and then used again in the context of a published geothermal-machine learning study. These key terms are divided into two groups, which are 1) essential to the field of machine learning but are predominantly absent in geoscience or 2) homonyms (i.e., words with the same spelling or pronunciation but with different meanings) between the fields. Lastly, we discuss a few other important homonyms that were not introduced in the general workflow but arise regularly in machine learning applications. 

Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(s) to represent very specific, precise, but, perhaps, different concepts. Under the best circumstances, misunderstanding key terms will lead toward a breakdown of efficiency. Under less optimal conditions, miscommunication will sow frustration, lead to errors, and inhibit scientific breakthroughs. Here, our research group of geoscientists and machine learning experts presents a process to help geoscientists understand the fundamentals of supervised learning by describing the general workflow (i.e., a conceptual pipeline) for supervised learning that must be understood by all the parties involved in a geoscience-machine learning endeavor. Terms critical for machine learning are introduced, defined, and used within the context of an overly simplified mock hydrological study to illustrate their appropriate usage, and then used again in the context of a published geothermal-machine learning study. These key terms are divided into two groups, which are 1) essential to the field of machine learning but are predominantly absent in geoscience or 2) homonyms (i.e., words with the same spelling or pronunciation but with different meanings) between the fields. Lastly, we discuss a few other important homonyms that were not introduced in the general workflow but arise regularly in machine learning applications. 

Abstract Magnetar giant flares are rare and highly energetic phenomena observed in the transient sky whose emission mechanisms are still not fully understood. Depending on the nature of the excited modes of the magnetar, they are also expected to emit gravitational waves (GWs), which may bring unique information about the dynamics of the excitation. A few magnetar giant flares have been proposed to be associated with short gamma-ray bursts. In this paper we use a new gravitational-wave search algorithm to revisit the possible emission of GWs from four magnetar giant flares within 5 Mpc. While no gravitational-wave signals were observed, we discuss the future prospects of detecting signals with more sensitive gravitational-wave detectors. In particular, we show that galactic magnetar giant flares that emit at least 1% of their electromagnetic energy as GWs could be detected during the planned observing run of the LIGO and Virgo detectors at design sensitivity, with even better prospects for third-generation detectors. 

Abstract Warming across the western United States continues to reduce snowpack, lengthen growing seasons, and increase atmospheric demand, leading to uncertainty about moisture availability in montane forests. As many upland forests have thin soils and extensive rooting into weathered bedrock, deep vadose‐zone water may be a critical late‐season water source for vegetation and mitigate forest water stress. A key impediment to understanding the role of the deep vadose zone as a reservoir is quantifying the plant‐available water held there. We quantify the spatiotemporal dynamics of rock moisture held in the deep vadose zone in a montane catchment of the Rocky Mountains. Direct measurements of rock moisture were accompanied by monitoring of precipitation, transpiration, soil moisture, leaf‐water potentials, and groundwater. Using repeat nuclear magnetic resonance and neutron‐probe measurements, we found depletion of rock moisture among all our monitored plots. The magnitude of growing season depletion in rock moisture mirrored above‐ground vegetation density and transpiration, and depleted rock moisture was from ∼0.3 to 5 m below ground surface. Estimates of storage indicated weathered rock stored at least 4%–12% of mean annual precipitation. Persistent transpiration and discrepancies between estimated soil matric potentials and leaf‐water potentials suggest rock moisture may mitigate drought stress. These findings provide some of the first measurements of rock moisture use in the Rocky Mountains and indicated rock moisture use is not just confined to periods of drought or Mediterranean climates. 

Abstract We present James Webb Space Telescope (JWST) and Hubble Space Telescope (HST) observations of the afterglow of GRB 221009A, the brightest gamma-ray burst (GRB) ever observed. This includes the first mid-IR spectra of any GRB, obtained with JWST/Near Infrared Spectrograph (0.6–5.5 micron) and Mid-Infrared Instrument (5–12 micron), 12 days after the burst. Assuming that the intrinsic spectral slope is a single power law, with F ν ∝ ν − β , we obtain β ≈ 0.35, modified by substantial dust extinction with A V = 4.9. This suggests extinction above the notional Galactic value, possibly due to patchy extinction within the Milky Way or dust in the GRB host galaxy. It further implies that the X-ray and optical/IR regimes are not on the same segment of the synchrotron spectrum of the afterglow. If the cooling break lies between the X-ray and optical/IR, then the temporal decay rates would only match a post-jet-break model, with electron index p < 2, and with the jet expanding into a uniform ISM medium. The shape of the JWST spectrum is near-identical in the optical/near-IR to X-SHOOTER spectroscopy obtained at 0.5 days and to later time observations with HST. The lack of spectral evolution suggests that any accompanying supernova (SN) is either substantially fainter or bluer than SN 1998bw, the proto-type GRB-SN. Our HST observations also reveal a disk-like host galaxy, viewed close to edge-on, that further complicates the isolation of any SN component. The host galaxy appears rather typical among long-GRB hosts and suggests that the extreme properties of GRB 221009A are not directly tied to its galaxy-scale environment. 

Abstract Object GRB 221009A is the brightest gamma-ray burst (GRB) detected in more than 50 yr of study. In this paper, we present observations in the X-ray and optical domains obtained by the GRANDMA Collaboration and the Insight Collaboration. We study the optical afterglow with empirical fitting using the GRANDMA+HXMT-LE data sets augmented with data from the literature up to 60 days. We then model numerically using a Bayesian approach, and we find that the GRB afterglow, extinguished by a large dust column, is most likely behind a combination of a large Milky Way dust column and moderate low-metallicity dust in the host galaxy. Using the GRANDMA+HXMT-LE+XRT data set, we find that the simplest model, where the observed afterglow is produced by synchrotron radiation at the forward external shock during the deceleration of a top-hat relativistic jet by a uniform medium, fits the multiwavelength observations only moderately well, with a tension between the observed temporal and spectral evolution. This tension is confirmed when using the augmented data set. We find that the consideration of a jet structure (Gaussian or power law), the inclusion of synchrotron self-Compton emission, or the presence of an underlying supernova do not improve the predictions. Placed in the global context of GRB optical afterglows, we find that the afterglow of GRB 221009A is luminous but not extraordinarily so, highlighting that some aspects of this GRB do not deviate from the global known sample despite its extreme energetics and the peculiar afterglow evolution.