An analysis of homogenization and differentiation of Canadian freshwater fish faunas with an emphasis on British Columbia
Bibliographic record
Abstract
Faunal homogenization and differentiation occur when geographic regions show increased or decreased, respectively, similarity to each other in species composition owing to introductions and extinctions or extirpations. I used species presenceabsence data for "native" (i.e., estimated species compositions before European settlement) and "total" (i.e., including nonnative fishes and extinctions) faunas to examine faunal similarity of freshwater fishes among aquatic ecoregions of British Columbia and among Canadian provinces and territories. British Columbia ecoregions showed faunal differentiation as the mean Jaccard's faunal similarity coefficient for total faunas was significantly less than that for native faunas (31.4% versus 34.9%), but some ecoregions showed homogenization (e.g., Vancouver Island and Columbia River ecoregions). Comparisons across Canada showed low but significant homogenization; average pairwise Jaccard's coefficient was higher in total versus native faunas (29.1% similarity versus 27.8%, respectively). British Columbia's fish fauna increased the most in similarity to other areas (except the three territories), with an average increase of 4.9%. Native faunal similarity patterns are part of Canada's natural heritage but are threatened by human-mediated increases in nonnative species and extinctions. This analysis provides a baseline to track changes in inter regional faunal relationships at different geographic scales.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".