Bibliographic record
Abstract
This thesis describes a new method for large-scale landscape connectivity analyses. The first part shows that a minor adaptation of the Circuitscape model can allow the creation of omnidirectional connectivity maps illustrating flow paths and variations in the ease of travel across a large study area. The method is demonstrated in a 24300 km2 study area centered on the Montérégie region near Montréal, Québec. The circuit model is run with overlapping tiles covering the study region. Current was passed across the surface of each tile in orthogonal directions, then the tiles were reassembled to create directional and omnidirectional maps of connectivity. The resulting mosaics provide a continuous view of connectivity in the entire study area at the full original resolution that clearly show variations in current flow driven by subtle aspects of landscape composition and configuration. The second part of this thesis explores ways to quantify the visual content presented in the omnidirectional connectivity mosaics using the Circuitscape resistance distance metric and assessing the complexity of all tiles using an automatic features detection software (SURF). It introduces an extension to the tiling method that can automatically map the complexity of patterns in omnidirectional connectivity maps produced using circuit theory. The method is used to analyze a large omnidirectional connectivity map of Canada created using the EOSD data set and made of over 14000 tiles of 625 km2 each. The resistance distance and complexity data are then used to explore connectivity patterns in Canadian ecozones and for the identification of Canadian connectivity regions. This effort develops a powerful new application of circuit models by pinpointing areas of importance for conservation, broadening the potential for addressing intriguing questions about resource use, animal distribution, and movement. Provided high-resolution data is available, it is now possible to map and quantify the complexity of connectivity patterns for species that have large home ranges, such as entire biomes or even whole continents. This new method will help to better focus research, conservation and preservation efforts as well as guide commercial activities such as forestry and mineral resources extraction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".