Nut cold hardiness as a factor influencing the restoration of American chestnut in northern latitudes and high elevations
Bibliographic record
Abstract
American chestnut ( Castanea dentata (Marsh.) Borkh.) was functionally removed as a forest tree by chestnut blight (caused by the fungal pathogen Cryphonectria parasitica (Murr.) Barr). Hybrid-backcross breeding between blight-resistant Chinese chestnut ( Castanea mollissima Blume) and American chestnut is used to support species restoration. However, preliminary evidence suggests that backcross material may not have the cold hardiness needed for restoration in the northern portions of the species’ range. The cold tolerance of nuts is of concern because reproductive tissues are particularly sensitive to freezing damage. We assessed nut cold tolerance for 16 American chestnut, four Chinese chestnut, and four red oak ( Quercus rubra L.) (a native competitor) sources to better assess genetic variation in nut hardiness. We found that Chinese chestnut nuts were less cold tolerant than American chestnut and red oak nuts and that American chestnut sources from the south were less cold tolerant than sources from the north, with significant differences among sources within all regions. We also assessed how sources varied among temperature zones (sources separated by average winter temperature lows at source locations). Sources from the cold temperature zone were more cold tolerant and less variable in hardiness than sources from warm and moderate zones.
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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) | 0.001 | 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".