Datasets for Comparison of methods for estimating omnidirectional landscape connectivity
Bibliographic record
Abstract
This repository holds the raster data and the configuration files (.ini) files used to create the simulated resistance grids and omnidirectional connectivity methods in "Comparison of methods for estimating omnidirectional landscape connectivity". There are reisstance grids for each of the two sections (simulated resistance grids and omnidirectional connectivity methods) and .ini files for each run with each method (point, wall-to-wall, and omniscape. The .ini files can be run in Julia computing language. Ecological connectivity is an important component of wildlife conservation. Omnidirectional circuit theory algorithms model the flow of electric current across a resistance grid from all directions, making them particularly useful for modeling connectivity of multiple or widespread species, or when source and destination sites are not specified, such as in the case of continuously distributed species. Objectives. We compared three published omnidirectional connectivity methods – the point-based method, the wall-to-wall method, and the Omniscape method. Methods. We compared the three omnidirectional connectivity methods using ten simulated resistance grids, representing commonly encountered landscape features. We then used three Canada lynx (Lynx canadensis) snow-track datasets from Ontario, Canada to validate the ability of each method to detect landscape use. Results. Current density maps generated by all three methods were highly correlated for most simulated landscapes, with the exception of uniform resistance grids. In uniform grids, without landscape features to guide current, each method revealed unique node-placement biases. All three methods produced similar connectivity maps for lynx, showing higher current at lynx presence locations compared to absence locations. The main differences between methods were not in their output, but in their implementation and use of computational resources. Conclusions. Our findings suggest that all of the three currently published omnidirectional connectivity models produce similar current density maps and can accurately map landscape connectivity. We provide guidelines for selecting a method based on factors such as computational resources, study area, and project goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.027 |
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".