Data from: Spatial and seasonal determinants of arthropod community composition across an agro-ecosystem landscape
Bibliographic record
Abstract
We used Malaise traps to sample arthropod community composition at biweekly intervals across an agroecosystem landscape in southern Ontario, Canada, to evaluate the relative impact of site spacing, seasonal timing, weather conditions, local plant community attributes, and agricultural extent across the local landscape on spatial and temporal variation in arthropod community composition. The 15 field sites sampled spanned a wide gradient of agricultural extent that in isolation had a strong impact on spatial variation in arthropod community composition. DNA barcoding of the samples identified >10,000 different arthropod BINs (Biodiversity Index Numbers). Local variation in plant community composition and canopy copver best explained variation in arthropod community dissimilarity. Weather variables followed closely in explanatory power, driven primariy by seasonal variation in temperature. Significant effects of time remained after accounting for environmental variables, with little independent effect of spatial distance. The proportion of agricultural land at the landscape scale had little detectable effect after controlling for the influence of the other environmental variables. Our results suggest that plant community composition, cover, weather conditions, and seasonality structured the arthropod community to considerable degree.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".