Observing the ‘Spheres with the EarthScope Transportable Array
Bibliographic record
Abstract
The motivation and objective of the EarthScope Transportable Array (TA) is to record earthquake signals and image the structure of the North American plate, however the observations collected by this National Science Foundation funded project have enabled unanticipated discoveries, innovative data analysis techniques, and ongoing investigations across many disciplines in the Earth and space sciences. The Transportable Array utilized a survey approach to collect data in which high-quality stations were systematically installed in a dense geospatial grid. From the very beginning of the deployment, this strategy allowed for data-driven discovery, such as using seismic data to map out extensive travel time curves for acoustic waves in the atmosphere (Hedlin et al., 2010). While the emplacement of the seismic sensors was kept uniform along with the core components for power and communications, the Transportable Array station design evolved over time to include additional barometric pressure and infrasound sensors and, eventually, meteorological sensors measuring external temperature, wind, and precipitation. As the array rolled across the Lower 48 and the TA became more recognized outside of seismology, collaborations were forged and strengthened with researchers in the infrasound and meteorological communities. Along with standard approaches using direct measurements, inventive techniques were used to apply environmental data for observing tectonic phenomena as well as applying seismic data for observing environmental phenomena. The value of integrated scientific infrastructure became even more apparent with the Transportable Array deployment in Alaska and western Canada, with autonomous and telemetered stations occupying sites within large swaths of previously unmonitored and inaccessible terrain. The majority of Alaska TA stations collect weather data and a subset also include a detached soil temperature probe. As a result, data collected by the Alaska Transportable Array have been used to observe throughout the ‘spheres: the lithosphere (earthquakes, volcanoes, landslides), the cryosphere (sea ice), the hydrosphere (precipitation, fire preparation), the atmosphere and biosphere (weather forecasting, storm systems, bolides), and even into the magnetosphere (space weather).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".