Economic implications of a native tree disease, Caliciopsis canker, on the white pine (<i>Pinus strobus</i>) lumber industry in the northeastern United States
Bibliographic record
Abstract
In the northeastern United States, eastern white pine (Pinus strobus L.) is a leading species in the forest products industry. The native pathogen Caliciopsis pinea Peck is associated with Caliciopsis canker of white pine, with symptoms including excessive resin production and cankers. This study processed 28.0 m3 of white pine lumber to (i) quantify losses resulting from Caliciopsis canker, (ii) assess how damage varies between Caliciopsis canker symptom severity and thinning, and (iii) quantify economic loss resulting from damage. Caliciopsis canker damage was present in 37% of lumber, yet only 10% was downgraded due to canker damage. Of the downgraded lumber, the vast majority (77%) lost one grade. Additionally, severely symptomatic trees consistently had more damage, and their lumber was more likely to be downgraded than trees with low symptom severity. Caliciopsis canker damage resulted in average revenue losses of 2.3%, yet much of the sampled lumber had other, more significant damage that resulted in downgrade: highly symptomatic trees averaged 63% of the revenue of low or asymptomatic trees. Caliciopsis canker, therefore, can be used as an indicator of poor quality trees. We recommend thinning Caliciopsis canker symptomatic trees to meet low-density stocking guidelines, which may minimize revenue loss while simultaneously minimizing stress to residual stock.
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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 | 0.001 | 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".