Interactions among defoliation level, species, and soil richness determine foliage production during and after simulated spruce budworm attack
Bibliographic record
Abstract
Defoliation level and site type are thought to influence tree response during spruce budworm (Choristoneura fumiferana (Clemens)) outbreaks. We determined the effects of four manual defoliation treatments (0%, 50%, 100%, and 100% + bud removal of current foliage) for 3 years on foliage production of balsam fir (Abies balsamea (L.) Mill.), black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.), and white spruce (Picea glauca (Moench) Voss) trees on four site-quality classes. After 3 years of defoliation and 2 years of recovery, foliage biomass was reduced by 34%–98%. During defoliation, the number of shoots generally increased and shoot length of spruce generally decreased, especially on rich sites. During recovery, the number of shoots increased substantially, shoot length decreased, and bud destruction reduced the number of shoots by about 50% compared with that of trees that received the 100% defoliation treatment. Defoliation did not substantially affect needle length. Trees on rich sites had two- to fourfold greater foliage production than trees on poor sites. Effects of site and defoliation differed among species, but site quality, especially nutrition, played an important role in production of shoots and needles and the tree’s ability to withstand defoliation. Black spruce had more limited ability to recover foliage biomass, only producing more shoots, whereas balsam fir and white spruce had stronger ability to recover needle and shoot length, respectively.
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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 | 0.000 |
| 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) | 0.001 | 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".