Use of Arthropod Diversity and Abundance to Evaluate Cropping Systems
Bibliographic record
Abstract
Economic viability and soil degradation are major issues facing farmers in the grassland ecozone of the northern Great Plains. Management strategies such as crop diversification, reduced fallow, and reduced inputs are being promoted as solutions. However, knowledge of the impacts of these management strategies on the grassland ecozone is lacking. Studies using a systems approach, applied as the experimental framework with which to monitor and assess alternate input and cropping strategies, are being conducted through the collaboration of crop, pest, economic, and soil scientists. Five examples are presented that highlight the arthropod (insects, spiders, and mites) component of multidisciplinary studies designed to evaluate crop management strategies. They demonstrate that arthropods are the most diverse group of organisms in the ecosystems studied and include beneficial and pest species. These studies attempt to utilize the arthropod assemblages to characterize the ecosystems that they inhabit. Ecosystem‐based, baseline arthropod faunas are integral to evaluating existing cropping practices and aid in the redesign of farming systems to make them economically viable and environmentally sustainable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".