Patterns of uncertainty in life-history and extinction risk for Arctic vertebrates
Bibliographic record
Abstract
Conserving Arctic wildlife will be challenging given the ongoing environmental changes in the region. In addition, there is a lack of fundamental biological information for many Arctic species, including a dearth of knowledge surrounding conservation threats and the risk of extinction. In this study, we gather all available data on research effort and life-history traits to assess the current state of scientific knowledge surrounding 389 Arctic vertebrate species. We also quantify patterns of species evaluation by the IUCN Red List, a global database of conservation risk used to measure success and prioritize resources in many conservation programs. We find that 10% of Arctic vertebrates have been the subject of no peer-reviewed studies during the last 30 years, and that we have little life history knowledge for many species. Arctic marine fishes are especially poorly known with an average of 3.5 (out of six) key life-history traits unknown. In a multivariate analysis, whether an Arctic species had been evaluated by the IUCN Red List was most strongly predicted by research effort and varied among taxonomic groups. In addition, we found that species that have been evaluated by the IUCN Red List continue to receive more research attention than species which have not been evaluated. Protecting all Arctic species may, therefore, require research programs and methods to halt research inertia and shift more attention onto species that are poorly known.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".