ecolo-zip: A global, rich and granular characterization of biogeophysical ecology for 1.5 million postal codes
Bibliographic record
Abstract
The inescapable fact that human life is perpetually embedded in a tangible biogeophysical environment - and the consequences that this has for individuals and societies - have long fascinated scholars of all backgrounds. Technological progress and the advent of big data have spurred ever-more precise attempts to quantify our biogeophysical environments. However, many such datasets lack spatial granularity, global coverage, content depth, or accessibility. Here, we introduce ecolo-zip, a novel geospatial dataset that provides a granular-yet-global, parsimonious-yet-rich ecological characterization of over 1.5 million postal codes across 94 countries and regions. Combining two large-scale satellite image resources (ASTER; SRTM, ICC = 0.999) and a customized geospatial sampling model, we provide high-resolution indicators of physical topography (elevation, mountainousness, distance to sea), vegetation (normalized difference vegetation index), and climate (surface temperature). With this resource - featuring methodological details, visualizations, and application suggestions - we hope to contribute towards understanding the multi-faceted interactions between humans and their environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".