Estimating spatial interactions in deforestation decisions
Bibliographic record
Abstract
Introduction Ongoing decreases in the stock of tropical forest have long been a major concern, due to their implications for biodiversity loss and provision of ecosystem services. Ecological research also provides evidence that even if the stock is held constant, the spatial pattern of forest affects the level of services generated (McCoy and Mushinsky 1994; Twedt and Loesch 1999; Diaz et al. 2000; Parkhurst et al . 2002; Coops et al . 2004; Scull and Harman 2004). A highly fragmented forest made up of small patches may not provide the minimum habitat size that some organisms require. Thus it may offer less protection for species than the same amount of unfragmented forest. It is then important to understand the effects of human activities that fragment standing forest and, as a result, alter the size, the shape, and also the spatial arrangement of habitat. These properties of habitat affect extinction rates of local populations. Standard economic models of rural land use (e.g. agriculture/forest frontiers) will generate predictions of spatial pattern down to the level of detail that their data permit. However, a focus on spatial pattern highlights a question these models do not address: are there spatial dynamics per se ? If we look behind observed spatial correlation, do one's land-use choices actually have any causal impacts upon those made by one's neighbours? This chapter presents a model of such spatial interactions and then discusses a method to empirically test for their presence using observed deforestation behaviour.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".