Land cover change analysis of the Yucatan Peninsula using landsat data from 1985 to 2010
Bibliographic record
Abstract
Local-scale vegetation change is one of the driving forces of global climate change, and forest loss in tropical countries is among the largest contributors to carbon emissions into the atmosphere. Lately several studies have employed Landsat data for forest change mapping but mainly focused on temperate regions. This study tests the Vegetation Change Tracker (VCT) in a tropical area. The northeastern section of the Peninsula of Yucatan was analyzed using 19 images of 30m Landsat data from 1985 to 2010. Accuracy assessment using reference polygons depicted high commission and omission errors, in many cases due to differences in spatial delineation of classified and visually-interpreted change polygons. The change rates were mostly smaller than 1% and thus in correspondence to other statistical sources. For areas with available secondary information, changes were linked to disturbance types, i.e. the cause of land cover change, which is an important information for carbon modelers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".