The effect of woodland proximity and wetland characteristics on larval anuran assemblages in an agricultural landscapeThis is contribution No. 82 of the MacArthur Agro-Ecology Research Center.
Bibliographic record
Abstract
Changes to landscapes for agricultural activities often result in reduction and fragmentation of forested habitat. Land conversion for cattle ranching in south-central Florida has resulted in increases in pasture land interspersed with remnant patches of hardwood hammock. To examine the importance of these hammocks to anurans, we sampled 78 seasonally inundated wetlands to examine the relative importance of proximity of hardwood hammock patches (>20 ha) and wetland characteristics and used generalized linear models to determine which factors had a significant effect on larval anuran species richness or abundance. Species richness was significantly influenced by pH, conductivity, and water depth. Proximity to hammock did not influence species richness; however, assemblage composition differed between wetlands near hammocks and wetlands surrounded by pasture. Barking treefrogs ( Hyla gratiosa LeConte, 1856), pine woods treefrogs ( Hyla femoralis Bosc in Daudin, 1800), and oak toads ( Bufo quercicus Holbrook, 1840) bred only in wetlands within 20, 50, and 200 m of hammocks, respectively. Factors influencing tadpole abundances were species-specific. Retention of seasonally inundated wetlands proximal to large hammocks on ranchlands can provide important habitat for supporting a diverse assemblage of anurans.
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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) | 0.001 | 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".