WHITE PINE WEEVIL ATTACK ON WHITE SPRUCE: A SURVIVAL TIME ANALYSIS
Bibliographic record
Abstract
The survival time of a white spruce tree (Picea glauca (Moench) Voss) with respect to the attack by the white pine weevil (Pissodes strobi (Peck)) was defined as the number of years until an attack occurred. In this context, resistant trees are those capable of delaying attacks rather than preventing attacks. This study investigated the patterns of survival times of resistant and susceptible trees in a progeny trial of white spruce in the interior of British Columbia, Canada. By doing so, we demonstrated that survival time analysis, a technique specifically developed for dealing with clinical medicine and industrial reliability tests, was a very useful approach in analyzing resistance of hosts to herbivore attack. The results suggested that survivorship of resistant trees was significantly different from that of susceptible trees, as expected. The median survival time of susceptible trees was ∼12 yr after seeding, while it was 21 yr for resistant trees. The survival times of white spruce trees were identified adequately following a lognormal distribution. Based on the estimated hazard functions of the lognormal distribution, the most severe weevil infestation was predicted to occur at 19 yr for susceptible trees, and at 26 yr for resistant trees. Infestation level was affected by several factors, such as tree height, seasonal temperature, and precipitation. An accelerated failure time model was used to evaluate the effect of these factors on survivorship. The identification of resistant or susceptible trees by survival times is to be incorporated into an integrated pest management program for the white pine weevil in British Columbia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".