ABoVE: Lakes and Ponds Weekly Occurrence, 2016 - 2023
Bibliographic record
Abstract
This dataset provides weekly surface water occurrence across lakes and ponds in six study regions: the Alaskan Coastal Plain, Yukon Flats, Yukon Kuskokwim Delta, Mackenzie River Delta, Tuktoyaktuk Peninsula, and Anderson Plain. The study regions are in Alaska, US, and Yukon and Northwest Territories, Canada. This binary (water or not water) raster product is built from Sentinel-2 imagery and a maximum lake extent vector product also produced from Sentinel-2 imagery. When cloud-free imagery was available, raster files were produced at 10 m spatial resolution and weekly temporal resolution during the ice-free season (May-September) for the period 2016-2023. Open water was differentiated from land using an adaptive NDWI threshold water classification algorithm and then clipped to the lake and ponds vector product so that only lake and pond water is reported. The data are provided in netCDF format along with two Jupyter notebooks holding code used for this analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".