Genetic variation in the frost hardiness of Pinus banksiana Lamb. (jack pine) in Northwestern Ontario
Bibliographic record
Abstract
To estimate the level and pattern of variation in frost hardiness, \nartificial freezing tests of 64 provenances of jack pine were conducted. \nThe provenances originated from northern Ontario. Seedlings of the \nprovenances were grown in a uniform environment in a shade house. \nCurrent-growth needles were collected in fall during three consecutive \nyears , 1988 to 1990, and in mid-summer in 1990. Three test temperatures \nand a control were used for all freezing trials. Temperatures ranged from - \n19? C to -1? C and duration varied from three to one hours. Freezing injury \nwas evaluated visually. Two way ANOVA indicated statistically significant \nprovenance and provenance x temperature interactions. These results \nsuggested that the tested jack pine provenances exhibited genetic \nvariation in their development of frost hardiness and implied a certain risk \nin transferring seed from one environment to another. Differentiation \namong provenances could not be detected during early August 1990. \nRegression analyses examined the associations between various degrees of \ninjury and climatic gradients. These analyses suggested that several \nselective forces, including precipitation and temperature, were partially \nresponsible for differentiation among the tested provenances. Principal \ncomponent analysis (PCA) of the data generated three significant principal \ncomponents which accounted for approximately 60% of the total variation. \nRegression of PCA scores against climatic gradients also reflected \nadaptive variation. However, a number of provenances which originated \nfrom regions with low temperatures and very short frost-free periods \nshowed no higher levels of frost hardiness than provenances from areas \nwith longer frost-free periods and higher temperatures. Low levels of \nconsistency were found among the different trials. Possible reasons for \nthe observed inconsistencies were assumed to be i) weaknesses of the \nscoring technique, ii) the random effect of supercooling and, iii) the uneven \ndistribution of temperature in the freezer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".