How does landscape structure influence landscape connectivity?
Why this work is in the frame
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Bibliographic record
Abstract
We investigated the impact of landscape structure on landscape connectivity using a combination of simulation and empirical experiments. In a previous study we documented the movement behaviour of a specialized goldenrod beetle ( Trirhabda borealis Blake) in three kinds of patches: habitat (goldenrod) patches and two types of matrix patch (cut vegetation and cut vegetation containing camouflage netting as an impediment to movement). In the current study, we used this information to construct simulation and experimental landscapes consisting of mosaics of these three patch types, to study the effect of landscape structure on landscape connectivity, using the T. borealis beetle as a model system. In the simulation studies, landscape connectivity was based on movements of individual beetles, and was measured in six different ways. The simulations revealed that the six measures of landscape connectivity were influenced by different aspects of landscape structure, suggesting that: (1) landscape connectivity is a poorly defined concept, and (2) the same landscape may have different landscape connectivity values when different measures of landscape connectivity are used. There were two general predictions that held over all measures of landscape connectivity: (1) increasing interpatch distance significantly decreased landscape connectivity and (2) the influence of matrix elements on landscape connectivity was small in comparison to the influence of habitat elements. Empirical mark‐release‐resight experiments using Trirhabda beetles in experimental landscapes supported the simulation results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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 it