Cold hardiness of white spruce, black spruce, jack pine, and lodgepole pine needles during dehardening
Bibliographic record
Abstract
Conifer winter damage results primarily from loss of cold hardiness during unseasonably warm days in late winter and early spring, and such damage may increase in frequency and severity under a warming climate. In this study, the dehardening dynamics of lodgepole pine (Pinus contorta Dougl. ex. Loud), jack pine (Pinus banksiana Lamb.), white spruce (Picea glauca (Moench) Voss), and black spruce (Picea mariana (Mill.) B.S.P.) were examined in relation to thermal accumulation during artificial dehardening in winter (December) and spring (March) using relative electrolyte leakage and visual assessment of pine needles and spruce shoots. Results indicated that all four species dehardened at a similar rate and to a similar extent, despite considerably different thermal accumulation requirements. Spring dehardening was comparatively faster, with black spruce slightly hardier than the other conifers at the late stage of spring dehardening. The difference, however, was relatively small and did not afford black spruce significant protection during seedling freezing tests prior to budbreak in late March and early May. The dehardening curves and models developed in this study may serve as a tool to predict cold hardiness by temperature and to understand the potential risks of conifer cold injury during warming–freezing events prior to budbreak.
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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) | 0.000 | 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".