Educating the Next Generation of Insect Rearing Professionals: Lessons from the International Insect Rearing Workshop, Mississippi State University, 2000–2017
Bibliographic record
Abstract
Insect rearing science and technology provide vital support for many areas of entomology and its applications, including basic and applied research, pest management (e.g., biological control, host plant resistance, and insecticide development), apiculture, public displays (e.g., insect zoos, butterfly houses), educational activities, and the nascent technology of insect production for feed and food. Consequently, insect rearing received increasing attention during the twentieth century and was explicitly recognized as a profession by Dickerson and Leppla (1992). In spite of this recognition, until the twenty-first century, insect rearing professionals received nearly all their training informally by working in insect rearing programs, networking with other professionals, studying insect rearing manuals and literature (e.g., Singh and Moore 1985, Anderson and Leppla 1992), participating in symposia at scientific conferences (e.g., annual meetings of the Entomological Society of America), visiting insectaries, and through trial and error (Cohen 2001). During the past 18 years, however, the demand for formal insect rearing education and training has been addressed primarily at two U.S. institutions: North Carolina State University and Mississippi State University.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".