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Record W3010574316 · doi:10.14720/aas.2020.115.1.1300

Association of traits in Ethiopian fenugreek (Trigonella foenum-graecum L.) genotypes regarding to seed yield by using phenotypic data

2020· article· en· W3010574316 on OpenAlexfundno aff
Yimam Ali Abtew, Alemu Abate

Bibliographic record

VenueActa agriculturae Slovenica · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
FundersAlberta Agricultural Research Institute
KeywordsTrigonellaPath coefficientPoint of deliveryBiologyYield (engineering)Biomass (ecology)Path analysis (statistics)Economic shortageIndirect effectAgronomyHorticultureVeterinary medicineMathematicsMedicineStatistics

Abstract

fetched live from OpenAlex

Shortage of information on association of traits is one of the problems in fenugreek productivity. Field experiment was implemented at Jamma district of South Wollo Administrative Zone of Amhara National Regional State, in 2018/19 main rainy season to examine the nature and extent of correlation, direct and indirect effects among yield and yield related traits. Sixty-two nationally collected fenugreek genotypes along with standard and local checks were evaluated in simple lattice design. Seed yield plot-1 was significantly and positively correlated to biomass yield-1 (r = 0.5) and harvest index (r = 0.6***) at genotypic level. Seed yield was also significantly and positively correlated with harvesting index (r = 0.6***) and weakly and negatively (r = -0.01) correlated to biomass yield at phenotypic level. Path coefficient analysis revealed that biomass (0.951), harvesting index (0.283) and pod length (-0.163) had contributed the maximum positive and negative direct effect on seed yield respectively, at phenotypic level. At genotypic level biomass yield ha-1 (0.816) and harvesting index (0.930) had contributed strong positive direct effect and plant height (-0.004) had revealed weak negative direct effect.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.938
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.229
Teacher spread0.164 · 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
Published2020
Admission routes1
Has abstractyes

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