Prioritizing recovery funding to maximize conservation of endangered species
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 absence of a rigorous mechanism for prioritizing investment in endangered species management is a major implementation hurdle affecting recovery. Here, we present a method for prioritizing strategies for endangered species management based on the likelihood of achieving species’ recovery goals per dollar invested. We demonstrate our approach for 15 species listed under Canada's Species at Risk Act that co‐occur in Southwestern Saskatchewan. Without management, only two species have >50% probability of meeting recovery objectives; whereas, with management, 13 species exceed the >50% threshold with the implementation of just five complementary strategies at a cost of $126m over 20 years. The likelihood of meeting recovery objectives rarely exceeded 70% and two species failed to reach the >50% threshold. Our findings underscore the need to consider the cost, benefit, and feasibility of management strategies when developing recovery plans in order to prioritize implementation in a timely and cost‐effective manner.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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