Pollen cone production in jack pine: spatial and temporal patterns subject to natural disturbance by the jack pine budworm
Bibliographic record
Abstract
Patterns of jack pine (Pinus banksiana Lambert) pollen cone production are of interest because they may help explain jack pine budworm (Choristoneura pinus pinus Freeman) outbreak patterns. We used generalized linear mixed models to analyze pollen cone production in 180 permanent plots in Ontario, Canada between 1992 and 2008. Pollen cone production increased with stand age, and large trees in sparsely-populated stands produced more pollen cones. Defoliation decreased the propensity of trees to produce pollen cones for at least two years. We also identified important patterns that are not explained by defoliation and stand characteristics. Pollen cone production is spatially synchronized among years, trees in central Ontario produced more pollen cones than trees in northwestern Ontario, and background cone production increased over time in the central region but not in more northwestern plots. Synchronized reproduction is common among tree species, but has not previously been noted for jack pine pollen cones. Increasing cone production in central Ontario may be evidence of changing forest and (or) climatic conditions and deserves further investigation. Our model can be used to quantitatively predict pollen cone production and assess the risk of jack pine budworm defoliation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".