Combining phylogenetic rarity with species distribution and abundance for conservation: Applying the 'calculus of biodiversity' in Canadian butterflies.
Bibliographic record
Abstract
Estimates of evolutionary rarity are important adjuncts to traditional parameters of biological rarity (distribution and abundance). I develop a taxonomic-based method for estimating ranked phylogenetic-abundance, using z-scores from natural-log-transformed species numbers at the sub-family level, for the two major superfamilies of Lepidoptera: Hesperioidea and Papilionoidea. These five categories of phylo-abundance were combined with equivalent estimates of geographic distribution and abundance ('geo-rarity'), at both global and sub-national (regional) scales using the Canadian butterflies (N=293 species). The model for prioritization used all nine possible combinations, (including global and regional, phylogenetic and geographic abundance values) gave priority co-equally to global geo-rarity and phylo-rarity, and then secondarily to regional rarity. Papilio brevicauda (Papilionidae) is Canada's overall highest priority for conservation (Group A). Evaluation of life history features revealed that wing span (increasing) dominates the discriminant function for global phylo-rarity and monophagy (presence) and sub-species numbers (decreasing) are discriminant functions for global geo-rarity.Dept. of Biological Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .T85. Source: Masters Abstracts International, Volume: 45-01, page: 0220. Thesis (M.Sc.)--University of Windsor (Canada), 2006.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".