ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014
Bibliographic record
Abstract
This record is for the dataset “ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014” at https://doi.org/10.3334/ORNLDAAC/1691 This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas. This dataset can be downloaded at https://doi.org/10.3334/ORNLDAAC/1691
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.023 |
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".