Estimating changes in global vegetation cover (1850–2100) for use in climate models
Bibliographic record
Abstract
Historical changes in global cropland area based on estimates of Ramankutty and Foley (1999), and projections of future changes under IPCC SRES development scenarios taken from the IMAGE 2 model, were combined with a simple classification of present‐day satellite data. These data were used to estimate annual changes in area fractions occupied by primary plant functional types (PFTs) between 1850 and 2100 using two different approaches. The linear interpolation approach assumed that natural vegetation area varies in inverse proportion to cropland area. The rule‐based approach added simple transition rules to define the sequence by which natural PFTs are converted to agriculture (e.g., grassland before forest) and by which abandoned cropland reverts to natural vegetation. In both approaches, constraints were imposed to ensure the simulated PFT composition was consistent with available information. The resulting time series data can be used in coupled biosphere‐atmosphere models, and in uncoupled global climate models, to represent time‐varying land cover.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".