National human footprint maps for Peru and Ecuador
Bibliographic record
Abstract
Human Footprint (HF) maps score human pressures based on their influence and integrate them into a single spatial index to assess the naturalness of ecosystems. We produced a historical series of national HF maps for Peru and Ecuador for Sustainable Development Goal 15 (SDG15) reporting. These maps integrate pressures from built environments, land use/land cover (LULC-agriculture, pasture, tree plantations), roads and railways, population density, electrical infrastructure, oil and gas infrastructure, and mining. The dataset includes HF maps and individual pressure maps for Peru from 2012 to 2021, as well as for Ecuador for the years 2014, 2016, 2018, 2020, and 2022. These maps support the analysis of spatiotemporal patterns of human influence at national and subnational levels, enabling biodiversity monitoring, modelling, and conservation in these highly biodiverse countries.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".