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Record W4413740659 · doi:10.53555//ar.v11i2.6330

IJRDO-Journal of Agriculture and Research

2025· paratext· en· W4413740659 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureAgricultural economicsGeographyArchaeologyEconomics

Abstract

fetched live from OpenAlex

Three improved varieties of soya (glycine max) subjected to an organic treatment based on cow dung were studied in buhimba in north kivu. The experimental design adopted was that of completely randomised blocks (crb) with 3 replications. The aim was to assess the yields of three soya varieties under the ecological conditions of buhimba. After applying 10t/ha of cow dung on the experimental site, the yields obtained were 7.727 t/ha ± 2.866 for the canada variety, 8.7 t/ha ± 2.107 for the sb24 variety and 6.983 t/ha ± 1.675 for the imperial variety considered as a control. After statistical analysis using a single-factor anova, comparison of the means at the 5% threshold revealed that there was no difference between the above yield averages. On the one hand, the soil in the study area, consisting of andosols, would be influenced by the application of cow dung, which would in turn have a positive effect on improving ph. Furthermore, the genetic make-up of these three varieties would have had some influence on their respective yields under similar conditions

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.081
Threshold uncertainty score0.994

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)0.0070.000

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 head0.060
GPT teacher head0.327
Teacher spread0.267 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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