Modelling biodiversity in the Grand River Watershed using a species distribution prediction approach
Bibliographic record
Abstract
This research examined and evaluated the use of GARP (Genetic Algorithm for Rule-Set Prediction) in combination with GIS (Geographic Information Systems), to effectively model biodiversity on the sub-watershed scale in Southern Ontario. To accomplish this, 11 environmental layers (based on topography, land cover, and climate) and georeferenced point data on 29 nationally ranked species at risk (plants, birds, reptiles, and amphibians) were obtained. Results indicated that five layers, (elevation, average summer precipitation, average annual temperature, average January temperature, and average July temperature) produced the most accurate results predicting species distributions (93.4%, 88.2%, 91.6%, 87.8%, and 92% accuracy, respectively). Using the spatial statistic measure AUC (Area Under Operating Characteristic Curve), an accuracy of 0.92 (max 1.0) using the best five layers combined in one model was reached compared to 0.91 using all 11 layers. Within the study area, a hotspot of biodiversity was projected stretching from Cambridge to approximately 15 km south of Brantford. These results aid in understanding the critical layers in prediction modelling, saving time and resources for future researchers in this area. Identifying the most biodiverse regions in the study area provides valuable information for land managers, as these areas often receive the highest priority for conservation. Lastly, this modelling approach can be applied to determine how species distributions will shift in response to climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".