Distributions of lizard species across edges delimiting open‐forest and sand‐mined areas
Bibliographic record
Abstract
Abstract The distributions of lizards across habitat edges delimiting open‐forest and regenerating sand‐mined areas as a function of distance from the edge were studied at Tomago, New South Wales, Australia. Pitfall‐trapping was used to survey lizards across the northern edges of four forest fragments, to determine if lizards displayed characteristic responses across the edge, and whether these could be explained by the different habitat conditions. At each site, 11 equally spaced drift fences (each parallel to the edge) were arranged in a transect running perpendicular to the edge, and stretching 50 m into each habitat type. Captures of Amphibolurus muricatus (Agamidae) decreased substantially across the edge from the mine‐path to the forest so that it was identified as a mine‐path specialist lizard species. Captures of two skink species decreased across the mine‐path before reaching the edge, and were not caught (Ctenotus taeniolatus) or were seldom caught (Ctenotus robustus) in the forest, so they were identified as mine‐path specialist, edge avoiding, lizard species. Captures of Lampropholis delicata (Scincidae) increased across edges into the forest, consistent with the expectation for a forest specialist. Regression analyses indicated the responses to edges of three lizard species (A. muricatus, C. robustus and C. taeniolatus) were negatively correlated with canopy cover (probably due to its influence on temperature, as captures of A. muricatus and C. robustus were also correlated positively with mean daily temperature). In addition, the response of C. robustus correlated negatively with a vegetation factor (dense, even vegetation in the first 50 cm from ground level). The response of L. delicata correlated positively with understorey height. We have identified edge response strategies for four species of lizards across edges delimiting temperate open‐forest and mined areas, and identified habitat and microclimate variables that may have driven these responses.
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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".