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Record W4405852032 · doi:10.5376/gab.2024.15.0032

Physiological Responses and Variety Screening for Drought Tolerance in Soybeans During Flowering and Podding

2024· article· en· W4405852032 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGenomics and Applied Biology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsDrought toleranceVariety (cybernetics)BiologyAgronomyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Drought tolerance is crucial for soybean cultivation due to its significant impact on crop yield and sustainability. This study aims to synthesize current research on the physiological, biochemical, and molecular responses of soybean to drought stress, with a focus on identifying traits and mechanisms that confer drought tolerance. The study examines the physiological responses to drought during the flowering and podding stages, including water relations, osmotic adjustment, stomatal conductance, transpiration, photosynthetic activity, and reproductive development. It also explores biochemical and molecular responses, highlighting antioxidant defense mechanisms, hormonal regulation, and gene expression related to drought tolerance. Furthermore, various screening methods for drought-tolerant varieties are discussed, encompassing field and controlled environment techniques, as well as the use of physiological and biochemical markers. Case studies of successful breeding programs and notable drought-tolerant soybean varieties are presented, alongside traditional and modern breeding strategies. This study provides a comprehensive understanding of the strategies employed by soybean plants to cope with drought stress, offering valuable insights for future research and breeding efforts aimed at enhancing drought tolerance in soybean. The findings are expected to inform breeding programs and contribute to the development of drought-resilient cultivars, thereby improving soybean production and food security.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.183

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 head0.025
GPT teacher head0.237
Teacher spread0.212 · 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