Estimates of catchment area of pitfall traps for carabids
Bibliographic record
Abstract
Relationships between beetle abundance in the field and data collected from pitfall traps has generated much interest from several generations of carabidologists. Despite the argument and controversy about this relationship, pitfall data remain the main source of knowledge about carabid populations and assemblages in nature. In order to estimate the relationship between pitfall catches of carabids and catchment area in a homogenous deciduous forest, we constructed circular field enclosures of three sizes (radii: 2.5m, 5m and 10m) and sampled carabids using single pitfall traps located in the centre of each enclosure over 3 seasons (2014-2016). We found that overall beetle catches increased linearly with enclosure area during all three years, and the linear models were nearly identical for the two years (2014 and 2016) in which the data were comparable. We then extrapolated the relationship to predict the catchment area using unenclosed traps run simultaneously. For data from both 2014 and 2016, the catchment area for the assemblage is predicted to be 620-640 m 2 , representing a radius of c. 14.3 m. Interestingly, this value is consistent with the published inter-trap distance required for independence of trap captures at the same site. Unfortunately, but as expected, calibration of pitfall data at the species level is challenging because the best linear model fits vary considerably among species. Trap catchment area also increased during breeding season. Clearly, pitfall traps need to be set at least c. 30 meters apart to ensure independence of samples in mixedwood boreal forest.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".