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Record W2013684313 · doi:10.5539/jas.v6n4p178

Evaluation of Maize Accessions for Nutrients Composition, Forage and Silage Yields

2014· article· en· W2013684313 on OpenAlexvenueno aff
J. T. Amodu, T. T. Akpensuen, D. D. Dung, R. J. Tanko, A. Musa, Sani Abubakar, M. R. Hassan, J. O. Jegede, Ibrahim Sani

Bibliographic record

VenueJournal of Agricultural Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsSilageForageSowingDry matterAgronomyBiologyComposition (language)NutrientLeaf area indexAccessionAnimal science

Abstract

fetched live from OpenAlex

An experiment was conducted to evaluate the yield of forage, silage and nutrients composition of two maize accessions: West Atlantic Seed Alliance 1 (WASA1) and an accession from Shika tagged (SHIMAZ). The maize seeds were planted in plots of 5 m x 5 m (25 m2) size with four replicates for each accession. The results showed that, there was no significant (P > 0.05) difference between the accessions for plant height and number of leaves at 4, 6, 8 and 10 weeks after sowing (WAS), but leaf area index (LAI) was significantly (P < 0.05) higher in SHIMAZ accession at 6 and 10 WAS. The number of leaves per plant, green leaves, leaf area and forage yield at 91, 105 and 119 days after sowing (DAS) were not significant (P > 0.05) in the two accessions of maize, but leaf area and LAI were significantly (P < 0.05) higher in WASA1 accession.. The percentage dry-matter (DM) content was significantly (P < 0.05) lower in WASA1, although leaf, and stem ratios were significantly (P < 0.01) higher at various stages of harvest. The crude protein (CP) and nitrogen free-extract (NFE) were significantly (P < 0.01) higher in WASA1 accession while crude fibre (CF) was significantly (P < 0.01) higher in SHIMAZ. The proximate components (ash, EE and CP) and NDF were significantly (P < 0.01) higher in WASA1 silage, but NFE and ADF were higher (P < 0.05) in SHIMAZ accession.

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)0.002
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 score0.970
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.036
GPT teacher head0.288
Teacher spread0.252 · 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 designObservational
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

Citations12
Published2014
Admission routes1
Has abstractyes

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