High-Resolution UAS-Based Optical and Thermal Infrared Imagery and Geospatial Data from Surveys of Lake Ontario Tributaries, New York (ver. 2.0, December 2025)
Bibliographic record
Abstract
This data release contains geospatial datasets that identify thermal zones across multiple reaches of four tributaries to Lake Ontario: St. Regis River, Salmon River, South Sandy Creek, and Sandy Creek. High-resolution uncrewed aircraft system (UAS)-based optical and thermal infrared imagery and ground level field observations are included. Shapefiles for each tributary reach identify thermal zones that are three, five, ten, and fifteen percent different than a reference temperature calculated as a mean along the approximate centerline of each reach. Also included are shapefiles of output from statistical geospatial models described in Woda and others (2025) for the same four tributaries plus Oak Orchard Creek, and Irondequoit Creek. These models predict where groundwater discharge to these tributaries is most likely and results were used to guide the selection of field sites for data collection and higher-resolution drone-based surveys. These data are accessible as an online webmap: https://ny.water.usgs.gov/maps/thermalrefugia First posted July, 2025, ver. 1.0 Revised December 4, 2025, ver. 2.0 Version 2.0 includes all the Version 1.0 data plus shapefiles of outputs from statistical geospatial models used to predict groundwater discharge and identify locations for UAS-based imagery collection. Additional geospatial attributes describing the source of groundwater discharge observed during foot surveys are also included. These data are accessible as an online webmap: https://ny.water.usgs.gov/maps/thermalrefugia
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.054 | 0.019 |
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".