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Record W2789333490 · doi:10.15544/rd.2017.062

AUTOMATIC CONCENTRATE DISTRIBUTION FOR FATTENING OF ROMANOV × DORPER LAMBS

2018· article· en· W2789333490 on OpenAlexaboutno aff
L. Šenfelde, D. Kairiša

Bibliographic record

VenueProccedings of International Scientific Conference "RURAL DEVELOPMENT 2017" · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal scienceBody weightWeight gainQuarter (Canadian coin)BiologyGeography

Abstract

fetched live from OpenAlex

The aim of this research was to study the possibility of using automatic concentrate feeding stations in fattening of lambs. Ten Romanov × Dorper weaned male lambs (initial live weight 21.0 ± 0.86 kg) for fattening were used. Lambs were kept indoors in separate pen and research was carried out in production conditions. Concentrate was distributed for animals individually in automatic feeding station. Adaption period were not applied, eight lambs had the concentrate intake in the automatic feeding station from first research day, one started eat concentrate from third research day and one – from eleventh day of research. The frequency of visits to automatic feeding station and daily concentrate intake was recorded and analyzed. Lamb’s were weighted before research and every fourteen days, live weight changes were analyzed. During all the research average number of daily visits to automatic feeding station of one lamb were 13 visits, average daily concentrate intake per animal was: 84 % of the average ration (1642 g) in all research period. Results shows, that average daily live weight gain was 246 ± 26.3 g, during last quarter daily live weight gain (89 ± 27.7 g) was significantly (p < 0.05) lover than in other quarters. For 1 kg lamb live weight gain 5.39 kg concentrate was used.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.886

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 science0.0010.000
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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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
Published2018
Admission routes1
Has abstractyes

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