Protocol for monitoring long-toed salamander (Ambystoma macrodactylum) populations in Alberta /
Bibliographic record
Abstract
This document is intended as a guide for studying long-toed salamanders, with emphasis on survey techniques and long-term monitoring.We recommend egg surveys as the best method by which to identify salamander presence, unless water turbidity limits visibility.Egg surveys can also be used as an indirect way to track population trends over time.Larvae searches can be used as an ancillary method to search for presence, but tend to be less reliable because larvae are mobile and cryptic.Capture methods include pitfall and minnow trapping, the former of which is recommended at permanent sampling sites.Minnow trapping is useful for capturing breeding adults and larvae in ponds where water turbidity prevents egg identification.Capturing adults is useful for gathering data on body parameters and general health, and is considered secondary to pond surveys.Markrecapture is the only method by which to gather data on population size or relative abundance, although it is a long-term, time-consuming process that is confounded by the stochasticity inherent in amphibian populations.Long-term monitoring of known breeding ponds is necessary to understand population trends and potential risks to populations.A combination of monitoring a subsample of ponds on an annual basis, and surveying all known ponds at five year intervals is recommended for the Alberta program.Surveying new ponds and commencing mark-recapture at an established longterm monitoring site is recommended.vi
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.020 |
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".