Comparison of methods to estimate historic species richness of mammals for tests of faunal relaxation in Canadian parks
Bibliographic record
Abstract
Aim Some recent tests of faunal change in reserves have relied on, but been limited to, estimates of species richness from random samples of historic range maps. We evaluated a different, Geographic Information Systems (GIS)‐based, approach to count species directly, as the latter method might facilitate rapid estimation of historic species richness as well as composition for samples of the same size, shape and exact location as present‐day reserves. Location National parks throughout Canada. Methods Geographic Information System. Results The GIS‐based method tended to count, in exact locations of modern parks, fewer species (on average, seven disturbance intolerant and five disturbance tolerant) present historically than extrapolated from randomly sampled sites, but the differences were not greater than expected by chance. However, correlations between number of species lost and park size were weaker than reported previously, suggesting a greater potential for other factors to influence a change in species richness (and composition) than inferred earlier. Main conclusions Direct counts of historic range maps using GIS tools provided a quick means to estimate site‐specific historic species richness not statistically different from estimates produced by random sampling. As well, the GIS‐based method could yield data about historic species composition for the specific location and size of modern reserves, which may be more ecologically meaningful in terms of assessing what factors may have contributed to the observed species losses.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".