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Record W2068971550 · doi:10.4141/p03-218

Alfalfa resistance to post-harvest <i>Aspergillus</i> species: Combining ability analyses

2004· article· en· W2068971550 on OpenAlexvenueno aff
C. A. Kimbeng, S. Ray Smith, Valerija Babij, K. M. Wittenberg

Bibliographic record

VenueCanadian Journal of Plant Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyDiallel crossRepensForageTrifolium repensAgronomyResistance (ecology)SilageHorticultureHayBotanyHybrid

Abstract

fetched live from OpenAlex

Hay making is still the most widespread technique of forage preservation. Saprophytic fungi, such as Aspergillus repens, can reduce forage quality during the post-harvest period by causing hay molding. Methods to control molding have yielded variable results, but genetic resistance has yet to be exploited. The objective of this study was to determine the mode of inheritance of resistance to post-harvest fungal growth in alfalfa. Seven parental genotypes representing a range of resistance to fungal growth (A. repens) were crossed in a complete diallel including reciprocals for a total of 42 crosses. Parental genotypes along with their progeny were screened for resistance to A. repens. The relative importance of general combining ability (GCA) and specific combining ability (SCA) effects in controlling resistance to post-harvest A. repens was compared. The results revealed a preponderance of GCA effects. The parent that supported the least amount of fungal growth produced progeny with the least amount of fungal coverage. The reverse was true for the more susceptible parents. The results indicate that breeding procedures that maximize additive gene effects, such as recurrent selection, could be effective in improving resistance to post-harvest fungal growth in alfalfa. Key words: Alfalfa, breeding, combining ability, resistance, Aspergillis repens, post-harvest fungi

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 score0.950
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.232
Teacher spread0.181 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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