The Value of Local Farms for Insect Conservation: Local Teaching Opportunities
Bibliographic record
Abstract
EVEN FARMS THAT SEEM TO LACK SUCH SPECIALIZED REGIONS PROVIDE UNIQUE HABITATS FOR INSECTS AND OTHER WILDLIFE. Farmland creates unique conservation challenges, including opportunities to increase biodiversity (Schieltz and Rubenstein 2016). Studies of local and small-scale farm systems are urgently needed, or the opportunity to understand the biodiversity of these areas will be lost. Increasingly, producers are under pressure to remove fencerows and hedges, install drainage tiles, and bring more land into production. Our study provides an example of the importance of studying small, overlooked farm habitats using simple, readily accessible methods and student help. Entomological field work is often taught using examples from our experiences working in exotic locations. Although these experiences can be fascinating, we wondered if such stories might convey a false impression that conservation is only important for wildlife in distant, fragile landscapes. Conservation is also very important at the local level. The goal of our project was to act according to this conviction, and to show that new or exciting data such as new distribution records could be found at a local farm close to the city.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".