Refining spatial neighbourhoods to capture terrain effects
Bibliographic record
Abstract
Spatially explicit ecological research has increased substantially in the past 20 years. Most spatial approaches require the definition of a spatial neighbourhood or the region over which spatial relationships are modelled or assessed. Spatial neighbourhood definitions impact analysis results, and there are benefits in considering neighbourhood definitions that better capture ecological processes. The goal of this research is to present a simple and flexible approach in constraining ecological spatial neighbourhoods using terrain data. Using watershed boundaries, we can restrict spatial neighbourhoods from combining populations or processes that should be separated by terrain effects. We demonstrate the need for ecological constraints by way of a simulation study and highlight our approach with a case study examining mountain pine beetle ( Dendroctonus ponderosae , Coleoptera; Hopkins) infestation hot spots. Our results demonstrate how failure to constrain neighbourhoods can lead to errors when the spatial signals from unrelated populations are mixed. Also, unconstrained spatial neighbourhoods can unintentionally detect spatial relationships across many scales. There will be benefits to studies that develop new, ecology-based approaches in defining spatial neighbourhoods that better illuminate ecological function of phenomena under study.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".