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Record W2759739245 · doi:10.6000/1927-5129.2017.13.83

Characterization of Elite Upland Cotton Genotypes for Earliness and Yield Traits

2017· article· en· W2759739245 on OpenAlexvenueno aff
Shahnaz Memon, Wajid Ali Jatoi, Nasreen Fatima Veeser, Nabila Kaleri, S. A. Khanzada, Nazia Kamboh, Lubna Rajput

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicResearch in Cotton Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsKharif cropBiologyRandomized block designCropHorticultureYield (engineering)AgronomyCrop yield

Abstract

fetched live from OpenAlex

Short duration cotton genotypes increased the chances to harvest crop before cold and rainy weather conditions. However, earliness in cotton is a difficult character, which is assessed by determining many plant traits. The current experimental was conducted at Cotton Section, A.R.I. Tando Jam, during kharif season 2013. The eight cotton genotypes including CRIS-342, Sindh-I, Haridost, Malmal, Bt-121, Bt-3701, TS-501 and Shahbaz were sown i n randomized complete block design with four replications. In a total, seven quantitative traits were measured such as days to first flowering, node number to set first sympodial branch, node number to set first flowering, sympodial branch length (cm), sympodial branches plant-1, number of bolls plant-1, seed cotton yield plant-1(g). The mean squares from analysis of variances showed that genotypes differed significant (P 0.01) for all the studied traits, demonstrating a vast genetic variability in tested genotypes. The genotypes, Sindh-1, CRIS-342, Bt-121 and Bt-3701 were characterized as early maturing or short duration cotton varieties. However, the correlation between yield and earliness traits were best criteria for developing the short duration cotton yield varieties.

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 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)0.001
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 score0.818
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.071
GPT teacher head0.294
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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