Resistance and tolerance of Douglas‐fir seedlings to artificial inoculation with the fungus <i>Ophiostoma pseudotsugae</i>
Bibliographic record
Abstract
Summary We tested the resistance and tolerance of 79 Douglas‐fir halfsibling seedling families to artificial inoculation with Ophiostoma pseudotsugae in a shade house experiment. The Douglas‐fir beetle vectors the fungus where it colonizes the phloem and sapwood, often leading to tree mortality. The 79 halfsibling seedling families originated from four seed planning zones in BC that span ecological gradients ranging from moist‐warm to cool‐wet. We tested resistance to the fungus by measuring lesion size and tolerance by measuring seedling height. We found variation in both resistance and tolerance within seed zones and halfsibling hierarchies. Trees from zones and families that were shortest before inoculation appeared to have the most tolerance after inoculation suggesting a cost for carrying tolerance traits. There was a cost in height growth for resistance after inoculation versus wounding alone. There was no trade‐off between family resistance and tolerance defence strategies indicating that both developed independently in the population. Higher resistance and lower tolerance to the fungus were the least commonly occurring trait combination in families. Douglas‐fir trees are moderately shade intolerant at the sapling stage, and as height increment is crucial for light capture, they probably avoid costly strategies such as resistance alone. This defence strategy may change in older stands.
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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".