Cold hardiness of interspecific hybrids between Pinus strobus and P. wallichiana measured by post-freezing needle electrolyte leakage
Bibliographic record
Abstract
Interspecific hybrids between eastern white pine (Pinus strobus L.) and Himalayan blue pine (P. wallichiana A. B. Jacks.) were developed in Ontario, Canada, to introduce blister rust (Cronartium ribicola Fisch.) resistance genes to P. strobus. There is concern that introducing blister rust resistance has resulted in reduced cold hardiness of the progeny compared with non-hybridized eastern white pine. To test the efficacy of backcrossing with P. strobus to improve cold hardiness, 1-year-old seedlings from hybrid crosses differing in P. strobus genome composition were artificially freeze-tested. In Experiment 1, unhardened seedlings were allowed to acclimate to progressively lower temperatures in a growth room, whereas in Experiment 2, seedlings were hardened outdoors under natural weather conditions in Sault Ste Marie, Ontario. Needle cold injury was determined by calculating relative electrical conductivity based on post-freezing electrolyte leakage. Results indicated that needle fascicles from unhardened seedlings of all genotypes in the greenhouse tolerated -5 degrees C for 3 hours with little or no injury. Cold hardiness increased in parallel with declining growth room minimum temperature over the 7-week period of hardening. Cold hardiness was improved for hybrid crosses with increased Pinus strobus genome composition in Experiment 2, but the results were less conclusive in Experiment 1.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".