Invisible people: Exploring how well remote-sensed datasets reveal the distribution of forest-proximate populations
Bibliographic record
Abstract
Abstract Accurate information on the location and density of people living at the forest frontier is vital for effective and equitable forest conservation. We compare the location of settlements and estimated population density from three global-scale, remote-sensed datasets (World Settlement Footprint 2015, Open Buildings, WorldPop) with a fine-scale, manually-derived dataset of 3,136 human settlements, of which 95% had fewer than 150 households. The study region is located in north-eastern Madagascar, contains three protected areas and the largest unprotected block of humid forest of the island. The Open Buildings dataset detected a much higher proportion (94%) of settlements than did World Settlement Footprint (15%). Population density from WorldPop matches poorly with that estimated from our manually-derived dataset. The accuracy of all three datasets is worse in more remote, forested areas, further away from basic infrastructure. Open Buildings appears to best reveal the distribution of low density scattered populations in forested areas. However, further testing in other climatic regions is still needed. Making good use of appropriate remote-sensed data could revolutionize the inclusion of local communities in conservation policy and practice, improve the quality of inference in conservation research, particularly in times of a planned expansion of the global protected area network.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".