Abiotic and biotic factors used to assess decline risk in red pine (<i>Pinus resinosa</i> Ait.) plantations
Bibliographic record
Abstract
This study was conducted to assess causes of unprecedented rates of mortality in maturing, commercial-sized red pine(Pinus resinosa Ait.) plantations in southern Ontario, Canada. Concentrated and diffuse mortality as well as windthrowof living trees were observed in many plantations, while others seemed disease-free. Nine sites exhibiting recent mortality(diseased) plus three not exhibiting disease (healthy) were selected. In sample plots at each site, abiotic site factors, hostcharacteristics, and insect pests and fungal pathogens were assessed. Tree mortality was attributed to Armillaria root disease,annosus root rot, black pineleaf scale, drought, and iron deficiency. The single abiotic factor that distinguishedhealthy sites from diseased sites was C soil horizon pH, which averaged 8.35 on diseased sites compared to 6.55 on healthysites. Rooting was also deeper on healthy sites than on diseased sites. We suggest that an alkaline C horizon may result inshallower rooting and greater susceptibility to drought stress, rendering trees less resistant to root disease pathogens andinsect pests. The pH of the C horizon and the depth of the A and B horizons may be useful as indicators of the likelihoodof red pine mortality and to guide the choice of management objectives for plantations. Key words: red pine mortality, root disease, calcareous soil, rooting depth, Armillaria
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".