Global perspectives and transdisciplinary opportunities for locust and grasshopper pest management and research
Bibliographic record
Abstract
Locusts and other migratory grasshoppers are transboundary pests. Monitoring and control, therefore, involve a complex system made up of social, ecological, and technological factors. Researchers and those involved in active management are calling for more integration between these siloed but often interrelated sectors. In this paper, we bring together 38 coauthors from six continents and 34 unique organizations, representing much of the social-ecological-technological system (SETS) related to grasshopper and locust management and research around the globe, to introduce current topics of interest and review recent advancements. Together, the paper explores the relationships, strengths, and weaknesses of the organizations responsible for the management of major locust-affected regions. The authors cover topics spanning humanities, social science, and the history of locust biological research and offer insights and approaches for the future of collaborative sustainable locust management. These perspectives will help support sustainable locust management, which still faces immense challenges such as fluctuations in funding, focus, isolated agendas, trust, communication, transparency, pesticide use, and environmental and human health standards. Arizona State University launched the Global Locust Initiative (GLI) in 2018 as a response to some of these challenges. The GLI welcomes individuals with interests in locusts and grasshoppers, transboundary pests, integrated pest management, landscape-level processes, food security, and/or cross-sectoral initiatives.
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 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".