Bibliographic record
Abstract
Variability in seedling death of legumes because of spring frost in the USA and Canada is associated with several factors. Experiments were conducted to evaluate factors that modify frost tolerance of seedling legumes. Experiment 1 was comprised of two hardenings, three freezing temperatures, eight legume species, and four growth stages. Experiment 2 had three temperatures, four durations of temperature, and four legume species. Experiment 3 included three soil types, two soil water levels, and two legume species. At each growth stage, legume seedlings were placed in a programmable freezing chamber at 3°C and temperature decreased/increased 1°C h−1 to and from a minimum freezing temperature. Duration of minimum freezing temperature was 1 h for Experiments 1 and 3 and varied according to the treatment in Experiment 2. Hardening increased seedling survival up to 40% over unhardened seedlings across growth stages and species. Forage legumes were more frost tolerant than soybean [Glycine max (L.) Merr.] and field pea (Pisum sativum L.) at all temperatures. Increase in duration of freezing temperature decreased the frost tolerance of all species when freezing temperature was near the LT50 (temperature that kills 50% of seedlings). Seedling survival of both alfalfa (Medicago sativa L.) and soybean was greater in light‐textured soil than the heavy‐textured soil with soil water at field capacity. However, one‐third of field capacity soil water allowed greater seedling survival in the heavy‐textured than the light‐textured soil. The results suggest that the factors studied should be considered to assess the frost tolerance of legume seedlings.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".