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Record W4385271459 · doi:10.18280/ijdne.180317

The Impact of Processing Methods and Levels of Jatropha gossypifolia L. Leaves on Productivity of Goats, Considering Distance

2023· article· en· W4385271459 on OpenAlexvenueno aff
Abdullah Naser, Padang Padang, Sagaf Sagaf, Sirajuddin Abdullah, Naharuddin Naharuddin, Sri Wulan, Nirwana Nirwana, Zainal Zainal, Mustafa Mustafa

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock Farming and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDry matterRandomized block designBiologyHematocritJatrophaAnimal scienceHemoglobinBiotechnologyAgronomyBiochemistryBiodiesel

Abstract

fetched live from OpenAlex

Jatropha gossypifolia L. is a critical source of feed ingredients for livestock, providing 60% protein with a proper balance of essential amino acids when appropriately processed.This study aimed to evaluate the production performance, physiological status, and haematological value of Nuts goats fed with jatropha leaves processed using different methods and levels.Thirty local female goats of 10 months old with a weight range of 10.89kg to 18.98kg were used.The research employed a 2×5 factorial randomized block design and was repeated three times.The analysis revealed an interaction between the processing method and the level of red jatropha leaves given to obtain body weight gain, consumption of dry matter, and crude protein ration.The processing method of red jatropha leaves had a significant impact on the consumption of dry matter and crude protein ration, body temperature, pulsus frequency, and hematological value (white blood cell count, red blood cell count, hemoglobin level, hematocrit value) of the goats.However, it did not affect the efficiency of using dry ingredients, protein rations, and the frequency of respiration of nuts goats.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.331
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same venueInternational Journal of Design & Nature and EcodynamicsSame topicLivestock Farming and ManagementFrench-language works237,207