Demographic analysis of the Columbia spotted frog (<i>Rana luteiventris</i>): case study in spatiotemporal variation
Bibliographic record
Abstract
This study examined size, mass, sex ratios, and age structure of the Columbia spotted frog (Rana luteiventris) at seven sites in the Toiyabe Range in central Nevada from 1994 through 1996. Age structure was estimated using skeletochronology. Demographic composition was found to be significantly different among sites, suggesting that localized factors influence recruitment and mortality rates. Demographics among years at the sites were also significantly different, indicating that the population dynamics of this system are complex and are also driven by one or more temporal factors. Knowledge of local land-use patterns and anecdotal observations were incorporated in an attempt to identify potential stress agents in need of further research and possible intensive management. Differences in recruitment, survivorship, and mortality rates among sites may be due to microclimate, food availability, and predation rates. Introduction of exotic trout and cattle are likely the most important anthropogenic factors limiting the distribution and persistence of R. luteiventris in the study area. Extreme variations in annual weather patterns may account for many differences observed at some sites. This study demonstrates that adequate assessment of amphibian population status requires knowledge of subpopulation demographics across a broad landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".