MétaCan
Menu
Back to cohort
Record W4393229444 · doi:10.53555/sfs.v8i3.2385

Nutritional Deficiency of Farm Animals: A Review

2022· review· en· W4393229444 on OpenAlexvenueno aff
Priyam Priya, Priyanka Kumari, Jayanti Ballabh, Mohsin Ikram, Gaurav Jain, Rajendra Prasad, Raja Joshi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRabbits: Nutrition, Reproduction, Health
Canadian institutionsnot available
Fundersnot available
KeywordsNutritional deficiencyMedicineMalnutritionInternal medicine

Abstract

fetched live from OpenAlex

The introduction of the article "Nutritional Diseases of Farm Animals" delves into the repercussions of modern agricultural practices on animal nutrition. It underscores the critical need to comprehend and prevent nutritional diseases in livestock. The text emphasizes the difficulties associated with intensive production practices, in which livestock are frequently lot- or stall-fed year-round on commercial feeds with little access to pasture. Nutritional deficiencies can result from poor feed selections or low-quality feed, which can set off significant nutritional disorders. The section emphasizes the vital importance of specific treatment and prevention strategies for these diseases, akin to approaches used for diseases caused by microorganisms or parasites. It also references the insights of Russell regarding the role of vitamins in curing diseases resulting from their deficiency. By shedding light on the impact of unwise feeding practices and the significance of proactive measures, the introduction sets the stage for a detailed exploration of nutritional diseases in various classes of livestock. Throughout the document, a focus on the interplay between nutrition, health, and agricultural practices underscores the critical role of proper nutrition in ensuring the well-being and productivity of farm animals (Payne et al, 2013).

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)0.012
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.329
GPT teacher head0.343
Teacher spread0.015 · 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
GenreReview

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicRabbits: Nutrition, Reproduction, HealthFrench-language works237,207