Analysis of land-use and land-cover changes in the Primavera do Leste Region, Mato Grosso, Brazil
Bibliographic record
Abstract
Abstract. The State of Mato Grosso (MT) in Brazil has experienced a rapid process of land cover conversion in recent decades, which is still poorly documented. Accurate information on land-use and land-cover (LULC) changes have crucial importance as they can greatly contribute to the understanding of impacts on the environment and the pursuing of a sustainable management of natural resources. The aim of this paper is to map historical LULC changes in the southeast part of the MT State (Primavera do Leste region), where the Cerrado (Brazilian savannas) has been intensively converted into agricultural land uses (crops/pasture). The methodology employed consists of a supervised classification approach for LULC mapping and a post-classification change detection technique for quantifying the changes. The results indicated an important loss of natural vegetation in the period from 1985 to 2005, with 45 % (7075 km2) of the Cerrado vegetation converted to agricultural land-uses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".