An evaluation of freezing tolerance of winter chickpea (<i>Cicer arietinum</i> L.) using controlled freeze tests
Bibliographic record
Abstract
Nezami, A., Bandara, M. S. and Gusta, L. V. 2012. An evaluation of freezing tolerance of winter chickpea ( Cicer arietinum L.) using controlled freeze tests. Can. J. Plant Sci. 92: 155–161. Chickpeas (Cicer arietinum L.) are subject to freezing injury and/or winter kill. Field testing for freezing tolerance evaluation is slow, unreliable, and highly variable; thus an artificial freeze test that correlates with field survival is required. Our objective was to develop a reliable and simple artificial freeze test to evaluate the freezing tolerance of winter chickpeas. Four cultivars with varying levels of freezing tolerance were grown and cold acclimated under low irradiance (150 µmol m−2 s−1) and high irradiance (400 µmol m−2 s−1). Acclimated whole plants or excised leaflets were subjected to six tests to determine the LT50 temperature (lowest temperature to kill 50% of the plants). In two tests, following the freeze test, whole plants were held at 20°C/15°C (day/night) for 3 wk for re-growth analysis. LT50 was estimated from both axillary buds and foliage re-growth and from foliage re-growth. The LT50 was also assessed on excised plantlets from whole plants frozen to a series of test temperatures. LT50 was determined by re-growth of plantlets held for 1 wk at 20°C in test tubes or by electrolyte leakage following thawing at 20°C. Excised plantlets were frozen to the same temperatures used for the whole plants. LT50 was determined by re-growth in test tubes for 1 wk or by electrolyte leakage. Results from excised plant parts from frozen intact plants or plantlets excised prior to the freeze test were similar to those estimates derived from re-growth analysis of plants frozen whole. Freeze test employing excised plantlets offers high precision and the ability to screen large populations. Plants grown and cold acclimated under an irradiance of 150 µmol m−2 s−1 were not as freezing tolerant as those grown and cold acclimated under an irradiance of 400 µmol m−2 s−1.
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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) | 0.000 | 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) | 0.000 | 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 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".