MétaCan
Menu
← Back to cohort
Record W3123335652 · doi:10.3920/978-90-8686-894-0_1

1. The neonatal pig: developmental influences on vitality

2020· book-chapter· en· W3123335652 on OpenAlexaff
C. Farmer, S.A. Edwards

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsCanadian Historical AssociationAgriculture and Agri-Food Canada
Fundersnot available
KeywordsVitalityBiologyWeaningPhysiologyAnimal science

Abstract

fetched live from OpenAlex

Newborn piglets are most vulnerable to postnatal stressors, such as competition for energy intake, crushing by the sow, and hypothermia, leading to the persistently high pre-weaning mortality rate that is currently seen in the industry. The present chapter describes the developmental and physiological factors that impact the vitality of neonatal piglets. Experiences in utero, such as placental blood flow and nutrient supply, are of great importance and can be influenced by external factors. Low birth weight constitutes a high risk for mortality and is exacerbated in piglets also showing intra-uterine growth retardation or hypoxia at birth. It has long been recognized that piglets are physiologically immature at birth. They have very low energy stores and poor immune protection. Some nutritional and hormonal treatments can be used to alleviate these problems, such as feeding fish oils or providing phytoestrogens to late-pregnant sows, yet no treatment has proved infallible. Piglets also have very little body fat and have a great surface to body mass ratio which makes them highly susceptible to hypothermia. The lower critical temperature of piglets is much higher than that of sows and this must be taken into account in the farrowing environment. Furthermore, intake of colostral immunoglobulins is essential for piglets to acquire systemic immunity, hence, any attempt to maximize this transfer should prove beneficial.

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: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.007

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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAdipose Tissue and Metabolism→French-language works237,207→