A beneficial arthropod dataset for agricultural landscapes in Western Canada, and adjacent mountain ecosystems
Bibliographic record
Abstract
One of the largest drivers of global biodiversity trends is land use change and habitat loss. Through several studies of beneficial arthropods, we have compiled a spatially extensive passive-sampling arthropod dataset for Western Canada focused on landscape diversity. This dataset, collected from 2015-2019, consists of more than 200,000 specimens, five arthropod orders, and 26 families of either pollinators (Hymenoptera, Diptera) or natural enemies of pests (Coleoptera, Araneae, Opiliones). In the research that collectively makes up this dataset, there are 409 sampling sites in two focal areas: the Canadian Rockies (n = 70) and the agriculturally intense Canadian prairies (n = 339). Sampling in the montane region focused on Bombus species, while both pollinators and natural enemies were sampled in the prairies. Within the prairie region, there was also a focus on non-crop habitat that occurs within or adjacent to the annual crop fields and rangelands that dominate the region. This data can be used to investigate beneficial insect abundance and richness over a gradient of elevation, land cover, landscape diversity and climate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".