Prioritizing threat management across terrestrial and freshwater realms for species conservation and recovery
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
Abstract The need to manage threats to biodiversity, and to do so cost‐effectively, is urgent. Cross‐realm conservation management is recognized as a cost‐effective approach, but it requires collaboration between agencies and jurisdictions, and local knowledge of anthropogenic threats to biodiversity. With its emphasis on stakeholder engagement and use of structured expert elicitation, Priority Threat Management (PTM) facilitates rapid, cross‐realm planning at the regional scale. We used PTM to identify cost‐effective management strategies with the aim of securing nine ecological groups, comprised of 45 species and one ecological community of conservation concern, across terrestrial and freshwater realms within the Wolastoq|Saint John River watershed in Canada. Under business‐as‐usual, four of nine groups are expected to have >50% probability of persistence over the next 25 years. Investment of $141 million over 25 years in three management strategies could secure seven groups across both realms with >50% probability of persistence. Achieving higher levels of persistence comes at a cost—securing six groups with >60% probability of persistence requires investing $218 million over 25 years in seven strategies. Through a structured, iterative process, whereby stakeholders cooperate to clarify objectives, devise management strategies, and collate data, PTM can support timely and cost‐effective management across multiple realms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it