The use of mulch to increase Spider (Arachnidae) numbers; a habitat approach to biological insect control
Bibliographic record
Abstract
The potential for insect predators to contribute to a biological balance of insect species was explored with mulch. Insects were collected in pitfall traps in outdoor microplots over 3 seasons in southern Ontario, Canada. Treatments varied each season with crops of oats or soybeans, with residue of straw, corn stalks or paperfibre, and with residue tilled in or surface applied. In 2006 at the peak of spider population density there was a significant effect of the plant and the paperfibre residue on increasing spider density. Existing data sets on spiders by Spiller and Schoener (1988 & 1994) were analyzed to assess the potential to increase spider survival from improved carrying capacity of their habitat. Spider census data was tested with curve fitting models in Aikaike Information Criteria (AIC). Spider populations of Metepeira, with sizeable numbers, were density dependent. Demographic data was assembled from Spiller & Schoener, 1988 and population size was projected with RAMAS Ecolab. Increasing the carrying capacity increased spider populations in the model projections. Mulch improves spatial diversity and could increase spider density from reduced intraspecific competition in the plot experiments. Increasing spider numbers with habitat complexity in agricultural systems could allow spiders to reduce specific pest problems through maintaining the balance of insect species.
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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 | 0.001 |
| 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".