Global 100m Terrestrial Human Footprint (HFP-100)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maps depicting the intensity of human pressure on the environment have become a critical tool for spatial planning and management, monitoring the extent of human influence across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. Here we provide an updated Human Footprint methodology to run on an annual basis to monitor changing anthropogenic pressures. Software and methods are parameterized to enable regular updates in the future. In addition, we release a 100-meter global dataset for the years 2015–2019 and 2020 based on land use, population, infrastructure, and accessibility data. Results show high levels of agreement in validation against expert-interpreted satellite imagery and improved performance compared to previous iterations of similar datasets. These maps are directly relevant to measuring progress towards national and international targets related to biodiversity conservation and sustainable development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.079 |
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 it