Bibliographic record
Abstract
Studies on the effects of agricultural intensity on anurans have found inconsistent results both between and within species, with varying conclusions about what could be driving these effects.One possible explanation for this variation is that agricultural intensity can be correlated with adult habitat amount.Another possible explanation is that differences in tadpole life history or sensitivity to agrochemical toxicity, may cause different responses to agricultural intensity because of its effects on larval habitat quality.We identified 39 ponds in Eastern Ontario, surrounded by a wide variation of row crop cover, where we measured anuran abundances, nitrate concentration, and a suite of local pond variables.We then constructed path analyses for each species to determine the direction and potential causes of effect of agricultural intensity on abundance.We found variable responses to agricultural intensity across anurans.As predicted, much of this variation could be explained by adult habitat amount and larval habitat quality.Overall, our results suggest that agricultural intensity itself is not strongly affecting anurans in our region, but confirm that habitat loss due to agriculture is particularly detrimental for forest species.We suggest that, since the term 'agricultural intensity' is used inconsistently in the literature, it should only refer to effects of increased chemical usage (i.e. for a particular crop type), independent of habitat amount.iii Appendix 11.Path analysis model fit criteria for Appendix 10: path analyses on effects of agricultural intensity on 'maximum recorded anuran abundance'.Several goodness of fit measures were included to assess the overall fit of each model, with boldness indicating a "good fit".Measures included: the X2 "badness of fit" measure (if p < 0.05, the model was a bad fit (Hair et al., 2006)), the comparative fit index (if CFI ≥ 0.90, the model was a good fit (Bentler, 1990)), the Tucker-Lewis index (if TLI > 0.95, the model was a good fit (Bollen, 1989)), and root mean square error (if RMSEA ≤0.07, the model was a good fit (Hu & Bentler, 1999)) ................................
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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 | 0.000 |
| 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) | 0.002 | 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".