Projet DS2 : Drosophila suzukii « Développer des Stratégies de gestion efficaces, économiquement viables et durables »Innovations agronomiques 94,127-140
Bibliographic record
Abstract
The DS2 project (2019-2022) evaluated several management methods for the pest Drosophila suzukii in cherry orchards and strawberry crops. A major study conducted on the trap plant Pyracantha coccinea showed strong potential in the laboratory but strong constraints during the first experiments in greenhouses. Management strategies based on the physical protection of cherry orchards by perimeter nets showed a potential in reducing phytosanitary interventions without causing side effects on crops. The development of the biological control method using exotic parasitoids was carried out through the identification of a species, Ganaspis cf. brasiliensis G1, very specific to the pest, promising trials in confined greenhouses and the finalization of an application file for the introduction of the parasitoid validated by the ministries. In addition, work to deepen our knowledge of the biology of the insect has led to a better understanding of the dynamics of fly populations and to complete the development of a model of egg-laying simulations that can be used as a decision-making tool to predict periods of risk on cherries.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".