Ice phenology for 3702 lakes and 1028 reservoirs across the Northern Hemisphere using Sentinel-2 imagery
Bibliographic record
Abstract
Ice phenology dates were extracted for 3702 lakes and 1028 reservoirs using Sentinel-2 data for four consecutive winters (2019-2020 to 2022-2023). The freeze-up and break-up dates of these water bodies were retrieved from Sentinel-2 imagery using an ice-detection algorithm through the Google Earth Engine platform from 2019 to 2023. The algorithm was validated by comparing the extracted phenology dates with an independent database based on observations from passive microwave sensors, with a mean absolute error of 18 days for both freeze-up and break-up dates. This newly established ice phenology database, along with various geographic, morphometric, and climatic characteristics of the water bodies, was used to develop a random forest model for predicting ice phenology dates. The predictive model demonstrates a satisfactory level of performance, with a mean absolute error of 12 days for both freeze-up and break-up. Furthermore, the random forest model is publicly accessible (see the Software section below). This dataset is stored in Excel format. The first sheet, titled "Data description," provides a detailed overview of all variables included in the dataset.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".