A comparison of techniques for assessing amphibian assemblages on streams in the western boreal forest
Bibliographic record
Abstract
A comparison of techniques for assessing amphibian assemblages on streams in the western boreal forest.Canadian Field-Naturalist 116(1): 116-119.The western boreal forest of Canada is rapidly being altered by agriculture, forestry, and energy extraction.As part of the Alberta Forest Biodiversity Monitoring Program's effort to develop monitoring schemes for biota associated with streams, we compared the performance of four sampling techniques for amphibians (constrained visual searches, call surveys, pitfall traps, and above-ground funnel traps) in June through August along two low gradient streams.Of the four anuran amphibian species that occur in the region, we encountered adults and young-of-the-year of two, Wood Frog (Rana sylvatica) and Western Toad (Bufo boreas), with the former species dominating our surveys.Given the time and equipment constraints imposed by the monitoring-program protocol, visual surveys proved to be the most effective technique (88.5% of amphibian records) for simply determining the presence of species.Pitfall traps performed better than funnel traps.Call surveys were the least effective technique principally because sampling took place after most breeding was completed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".