Impact of three silvicultural treatments on weevil incidence, growth, phenology, and branch-level dynamics of Pinus strobus from large and small populations
Bibliographic record
Abstract
Our goal was to quantify tree- and branch-level components of growth response to three silvicultural treatments (STs), and weevil incidence in white pine ( Pinus strobus L.) from large, central populations in Ontario (ON) versus small, isolated populations in Newfoundland (NL). Light levels were 100%, 42.0%, and 20.4% transmittance for the full-sun, intermediate-shade, and high-shade STs. After 8 years, the overall incidence of weevil infestation was 42.1%, 23.4%, and 13.7% for the full-sun, intermediate-shade, and high-shade STs, respectively (P < 0.001). Weevil impact on total height and volume averaged –13.2% and –11.8%. Analysis of dominant lateral shoots showed that ON populations had 32% longer shoots than the NL populations. Bud set difference was a primary determinant of shoot-length growth differences between regions: Julian days 171 and 184 for the NL and ON populations, respectively. The primary determining factors related to shoot length were the number of needle bundles and region, driven by light levels and day length, respectively, and the internode length, probably through inbreeding effects. Total height and diameter showed a positive curvilinear relationship to light level. To maximize fitness, NL conservation strategies should also now consider introducing adaptive trait variation in the context of anticipated climate change.
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 | 0.000 | 0.000 |
| 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".