Understanding rarity: A review of recent conceptual advances and implications for conservation of rare species
Bibliographic record
Abstract
Rare species carry a connotation of uniqueness, of being especially valuable, and of heightened extinction risk. We review the literature regarding rare species and link rarity and risk concepts to jurisdictional rarity and how to allocate conservation efforts to rare species gone long undetected. Conservation actions for rare species should be prioritized based on best available information of population trends and thresholds of minimum viable population or geographic range size. For species rare in some geopolitical jurisdictions but common elsewhere, we recommend prioritizing conservation action by assessing beyond jurisdictional boundaries to assess stewardship responsibility relative to the global distribution and at-risk status of the species in question. For making the thorny decision about when to stop managing or monitoring a long-undetected rare species, it may be optimal to continue conservation efforts for a long time, especially if the species has considerable social, economic or ecological value. Recent advances based on theories of optimality provide a replicable and transparent process upon which these decisions can be based.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".