Review of the Beekeeping Vigilance Methods and Perspectives
Bibliographic record
Abstract
A collapse of bee colonies, for the first time in Morocco, of unknown origin, was announced on January 21, 2022 by the National Office for Food Safety (ONSSA). Causes were mentioned such as the unprecedented drought affecting the country and climate change as well as the use of pesticides and the appearance of new hive parasites and viruses. Colony collapse disorder (CCD) has been reported in several parts of the world, including Europe, Canada and Asia. Due to the agro-economic and ecological interest of the bee in the pollination of fruit trees and various seasonal and annual crops, the condition is considered alarming. Hence the need for an integrated control program that uses various monitoring measures and a set of mechanical, physical, biological and chemical control methods against hive pests. A census of the observation and countermeasures of Morocco and at the level of the world in the face of this problem was carried out. Viral diseases (Dicistroviridae in the United States) or parasites (in Spain, the fungus Nosema cerenae, varroa destructor in Canada, Varroa, itself vector of the virus), livestock management problems (transhumance and inbreeding) and the use of pesticides (the Gaucho, banned on sunflower since 1999 in France) remain the most studied causes. Surveillance and vigilance networks for beekeepers whose objective is to continuously inform the beekeeping sector of the general state of health of bees (mortality, theft of hives, invasions by other insects and parasites, etc.) everywhere in the world are essential. The ApiVigi® network is an example.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".