Will this umbrella leak? A caribou umbrella index for boreal landbird conservation
Bibliographic record
Abstract
Abstract Conservation approaches that efficiently protect multiple values, such as the umbrella species concept, have been widely promoted with expected dramatic ecosystem changes. Due to its social and cultural importance, and recent declining trends, boreal populations of woodland caribou have been suggested as potential umbrella species for other declining taxa, such as boreal landbirds. We propose a generic pixel‐based umbrella index that focuses on fine‐grained habitat overlaps. In light of ongoing conservation efforts worldwide implementing area‐based targets (e.g., 30% by 2030), we used a random neutral model as baseline, as opposed to a no‐conservation scenario, which has been used elsewhere. We found that the conservation efficiency of caribou as an umbrella for 71 co‐occurring landbirds—three of which are priority species—in the Northwest Territories, Canada, is generally lower than our random model, as 53% of the species presented negative umbrella index medians with the interquartile range not overlapping zero. We conclude that in cases where area‐based targets drive decision‐making and the issue at stake involves identifying which areas to conserve—not whether to conserve—woodland caribou may be a leaky umbrella for most co‐occurring landbird species and these might need complementary conservation actions to be brought in from the rain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".