Situation de l’épicéa commun (Picea abies) liée aux attaques de scolytes typographes (Ips typographus) en région Bourgogne – Franche-Comté à l'été 2024 : une épidémie toujours active dans le massif jurassien
Bibliographic record
Abstract
The epidemic of bark beetles (Ips typographus), which began in 2018 and is affecting spruce forests in the region, intensified significantly in the Jura massif in 2023, particularly in the Haut-Jura, including in the natural range of the common spruce (above 1000 metres altitude). In the Jura massif of the Franche-Comté region, it is estimated that around 10% of the surface area of spruce and fir trees has been affected by bark beetle since 2018.At the beginning of April, exceptionally mild temperatures for the period led to a massive and early take-off of bark beetles on the plains and in the mountains, suggesting a further intensification of bark beetle attacks. However, the return of regular and abundant rainfall from mid-April to mid-July could improve the health situation, although we must remain cautious about this effect given the very high bark beetle populations in the Jura mountains. The weather conditions at the end of the summer and this autumn will be decisive in determining how the epidemic, which is currently at its worst, develops.Outbreaks of bark beetles linked to the attacks of this spring/early summer will continue to appear over the coming weeks, and the full extent of this summer's attacks will not be revealed until vegetation resumes in the spring of 2025.Preventive and curative measures against bark beetles need to be implemented now more than ever to accelerate the potential decline in attacks by taking advantage of these favourable weather conditions for spruce trees. The stakes are very high in the Haut-Jura forest region, where most of the region's spruce stands cover several tens of thousands of hectares. The probable increase in parasitoid predators of bark beetles, correlated with the duration of the epidemic, may also play a regulatory role, although it is difficult to measure this impact.Diversification of the structure and composition of forest stands must take place at all altitudes to increase resilience in the face of such health crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".