Land Use and Cover in the Southeastern United States, 1987-2019 (0.25 degree)
Bibliographic record
Abstract
These are quarter degree datasets that contain the percent of nine land use classes in each quarter degree cell covering the Southeastern United States. Classes are designed to be compatible with the classes in the Land-Use Harmonization 2 dataset (LUH2) (Chini et al. 2021). This dataset includes a new 'actively-managed forest' class that represents pine plantations that are being actively managed, using forest thinning as a proxy for active management. Thins are identified based on the Landsat analysis outlined in Thomas et al. (2021), but applied in Google Earth Engine to the entire Southeastern U.S. in three year increments from 1987-2019. Note that the aggregate area of active management should be interpreted as the total number of pixels within a 0.25 degree cell that were identified as active management at some point during the time period, but may not have been actively managed the entire time period. The other 8 LUH-compatible classes were generated by aggregating the 2016 release of the National Land Cover Dataset (NLCD) (Homer et al. 2020, Jin et al. 2019) for the most recent corresponding year, based on the reclassification scheme described in the readme file.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".