Agreeing that maps can disagree: Moving away from map confusion in conservation
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
Deciding where to implement actions for biodiversity conservation remains challenging for many reasons, including the increase in maps aimed at prioritizing locations for conservation efforts. Although a growing numbers of maps can create the perception of uncertainty and competing science, a shared set of principles underlie many mapping initiatives. We overlaid the priority areas identified by a subset of maps to assess the extent to which they agree. The comparison suggests that when maps are used without understanding their origin, confusion seems justified: The union of all maps covers 73% of the contiguous United States, whereas the intersection of all maps is at least 3.5%. Our findings support the need to place a strong focus on the principles and premises underpinning the maps and the end users' intentions. We recommend developing a science-based guidance to aid scientists, policymakers, and managers in selecting and applying maps for supporting on-the-ground decisions addressing biodiversity loss and its interconnected crises.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it