Bibliographic record
Abstract
Context: Landscape fragmentation, which has demonstrated links to habitat loss, increased isolation, a loss of connectivity, and decreased biodiversity, is difficult to quantify. Traditional pattern-based approaches to measuring fragmentation use landscape metrics to quantify aspects of the composition or configuration of landscapes. Objective: The objective of this study was to examine the relative improvements of an alternative activity-based approach using the cost of traversing a landscape as a proxy for fragmentation and compare it with the traditional approach. Methods: One thousand binary landscapes varying in composition and configuration were simulated, and least-cost path analysis provided the data to calculate the activity-based metrics, which were compared with computed traditional pattern-based metrics. Results: Activity-based fragmentation assessments were sensitive to levels of landscape fragmentation, but offered improvements over exiting pattern-based methods in that some metrics varied monotonically across the spectrum of landscape configurations and thus makes their interpretation more holistically meaningful. Conclusions: This study provides a modular conceptual framework for assessing fragmentation using activity-based metrics that offer functional improvements over existing pattern-based approaches. While we present a focused theoretical implementation, the process to be measured and the scale of observation can be altered to suit specific user requirements, ecosystems, or species of interest.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".