An Overview of Cold Hardiness in Woody Plants: Seeing the Forest Through the Trees
Bibliographic record
Abstract
D. Siminovitch and his graduate student, Keith Pomeroy also were among the first to document that the protoplasm underwent distinct biochemical changes during cold acclimation that presumably played a direct role in conferring stress tolerance (Pomeroy and Siminovitch, 1971).Research by the Russian scientist I. Tumanov (as reviewed in Sakai and Larcher, 1987) also recognized the importance of biochemical changes during cold acclimation.Truly, the research conducted by these pioneers in the first half of the century formed the conceptual basis of much of what was to follow.Regarding cold hardiness research, it is also important to recognize the important contribution that was made by the Plant Cold Hardiness Laboratory at the University of Minnesota, St. Paul, beginning in the 1960s, under the leadership of Conrad J. Weiser.Research (as reviewed in this article) conducted by scientists and graduate students at this laboratory dominated the literature for over 25 years .A greater understanding of deep supercooling, the biophysics of water at low temperatures, dormancy, the role of sugars in cold hardiness, and more, all grew from the activities of this laboratory.People such as
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".