Distribution of Cranberry Blue Butterflies (Agriades optilete) and Their Responses to Forest Disturbance from In Situ Oil Sands and Wildfires
Bibliographic record
Abstract
Cranberry blues (Agriades optilete) are butterflies of conservation interest worldwide. Less than 20 populations are known in Alberta, Canada, mostly inhabiting boreal forests that are increasingly fragmented by oil sands developments and subject to wildfires. We modeled the abundance of cranberry blues in the boreal forests of Alberta’s Wood Buffalo Region as a function of forest characteristics, presence of disturbances associated with in situ oil sands exploration, and wildfire disturbance, while accounting for butterfly detectability as a function of sampling conditions. We counted 188 cranberry blues during 1280 samples, discovering 14 unknown populations using a species distribution model based on forest wetness and canopy height. Probability of detection peaked around 5th July, and at higher temperatures and in the absence of wind, with cranberry blues preferring wetter treed peatland forests with low canopy heights. Seismic lines were positively related to the abundance of cranberry blues (400% increase), while exploratory well pads and wildfires were negatively related (60% and 90% loss, respectively). Overall, cranberry blue populations are small and locally sensitive to both natural and anthropogenic disturbances. Despite a narrow habitat specificity, cranberry blues seem more widely distributed than previously thought in northern Alberta (57% of the study area deemed suitable).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".