Lakes, Ponds, and Partial Inundation in the Yukon Kuskokwim Delta, AK, 2019-2023
Bibliographic record
Abstract
This dataset provides high resolution (3 m) maps delineating open water and partial inundation in the Yukon-Kuskokwim Delta (YKD), Alaska, and a database that tracks rapid changes in surface area for individual lakes and ponds over 2019-2023. The dataset was built using daily PlanetScope imagery (3 m), allowing for tracking changes in surface water extent at high spatial and temporal resolutions. Two types of maps were produced; (1) open water lakes and ponds and (2) partial inundation, defined as areas that partially contain water (e.g. emergent vegetation, flooded vegetation, or saturated soil). Open water lakes and ponds were mapped using a convolutional neural network, and partial inundation was classified using a pixel-based thresholding approach. For both map categories, monthly climatological composites for the snow and ice-free period are provided. For open water lake and pond maps, monthly composites are provided as well. An accompanying database includes the geometry and surface area time series for each mapped water body.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".