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Record W2162952006 · doi:10.1071/cp11218

High temperature tolerance in chickpea and its implications for plant improvement

2012· article· en· W2162952006 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrop and Pasture Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsBiologyGermplasmLegumeHeat stressMonogastricAgronomyPlant breedingPoint of deliveryRuminantCropYield (engineering)Animal science

Abstract

fetched live from OpenAlex

Chickpea (Cicer arietinum L.) is an important food legume and heat stress affects chickpea ontogeny over a range of environments. Generally, chickpea adapts to high temperatures through an escape mechanism. However, heat stress during reproductive development can cause significant yield loss. The most important effects on the reproductive phase that affect pod set, seed set and yield are: (1) flowering time, (2) asynchrony of male and female floral organ development, and (3) impairment of male and female floral organs. While this review emphasises the importance of high temperatures >30°C, the temperature range of 32–35°C during flowering also produces distinct effects on grain yield. Recent field screening at ICRISAT have identified several heat-tolerant germplasm, which can be used in breeding programs for improving heat tolerance in chickpea. Research on the impact of heat stress in chickpea is not extensive. This review describes the status of chickpea production, the effects of high temperature on chickpea, and the opportunities for genetic improvement of chickpea tolerance to high temperatures.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.214
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it