A Guide to Historical Data Sets for Reconstructing Ecosystem Service Change over Time
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
Ecosystem services (ES) span the interface of social and ecological systems, which makes them inherently challenging to measure. Tracking ES patterns over long time frames is crucial for understanding slow variables and complex interactions, but long-term studies of ES are rare. Historical records can play an important role in revealing temporal patterns of ES, but because they rarely measure ES directly, historical ES reconstruction presents new practical challenges. Furthermore, long-term data are limited in availability, quality, and structure. We review the utility, strengths, and challenges of some unconventional historical data sets with the potential for long-term ES tracking (e.g., aerial photography, oral history, tree rings.). We link each type of data to a simple ES framework that distinguishes ES capacity, ES flows, and ES demand. Using multiple historical data sets in parallel may enhance our understanding of ES sustainability and ES interactions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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