Bibliographic record
Abstract
How plants adapt to freezing temperatures and acclimate to survive the formation of ice within their tissues has been a subject of study for botanists and plant scientists since the latter part of the 19th century. In recent years, there has been an explosion of information on this topic and molecular biology has provided new and exciting opportunities to better understand the genes involved in cold adaptation, freezing response and environmental stress in general. Despite an exponential increase in our understanding of freezing tolerance, understanding cold hardiness in a manner that allows one to actually improve this trait in economically important crops has proved to be an elusive goal. This is partly because of the growing recognition of the complexity of cold adaptation. The ability of plants to adapt to and survive freezing temperatures has many facets, which are often species specific, and are the result of the response to many environmental cues, rather than just low temperature. This is perhaps underappreciated in the design of many controlled environment experiments resulting in data that reflects the response to the experimental conditions but may not reflect actual mechanisms of cold hardiness in the field. The information and opinions presented in this report are an attempt to illustrate the many facets of cold hardiness, emphasize the importance of context in conducting cold hardiness research, and pose, in our view, a few of the critical questions that still need to be addressed.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".