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Record W6925401290 · doi:10.17632/s7v6nnj852.2

Meta-Analysis dataset: Methionine Sources for Weaned pigs

2023· dataset· en· W6925401290 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2023
Typedataset
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsMajestyContext (archaeology)ScopusPopulationSubject (documents)Statement (logic)Publication biasSelection (genetic algorithm)

Abstract

fetched live from OpenAlex

The datasets generated in the trials and by simulation during the current study belong to His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. Data use is subject to obtaining permission of the representative of His Majesty the King in Right of Canada. Please contact the author for further information. Data collection (search strategy and inclusion criteria) A systematic review was conducted using the PRISMA statement (Page et al., 2021) to update the dataset previously used in a meta-analysis (Remus et al., 2015). The platforms Pubmed (126 references), ProQuest (235 references), Scopus (143 references) and Science Direct (1,663 references) were searched to identify studies reporting experimental dose-response results for the different Met sources used in pigs from weaning to finishing. The review question was proposed using the “PICo” framework, where a set of keywords was created, including elements designating population (pigs, piglets), interest (Met requirements, Met supplementation), and context (growth performance). After 293 duplicate references were removed, 1,898 studies remained. These studies were analyzed according to the following selection criteria: 1) research must have been conducted on pigs; 2) the paper must have been published between 1990 and 2021; 3) and it must present growth performance data. Based on these criteria, 1,779 papers were eliminated after the titles were assessed, while another 74 were eliminated after reading the abstract. An additional search in Google Scholar was performed. The keywords used were “pigs,” “Met,” and “growth,” and 24 adittional papers were selected according to the three previously stated criteria. At this step, the database comprised 45 references that were individually screened for full-text analysis. The inclusion criteria were as follows: 1) Studies must present treatments with different sources of free Met such as L-Met, DL-Met and/or OH-Met; 2) Free Met must have been provided at one or more inclusion levels; 3) Met must be the first limiting amino acid according to the material and methods; 4) Papers must present the nutritional composition of the experimental diets. A total of 33 studies were retained for the meta-analysis at the end of the evaluation process. Due to the small number of studies on the growing and finishing phase, only the post-weaning phase (5-25 kg body weight) was included in the analysis. Post-weaning data were present in 24 papers used in the meta-regression and meta-analysis. The ingredient composition of each diet was computed in spreadsheets and used to estimate (recalculate) the dietary composition in terms of net energy, SID AA, as well as mineral composition using the software EvaPig®. When available, the analyzed total AA composition provided by the authors was used to estimate SID values with EvaPig®. The codes used in the database are described on the Legend sheet.

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)0.012
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.013
Bibliometrics0.0090.013
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0430.006

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 head0.186
GPT teacher head0.305
Teacher spread0.118 · 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 designMeta-analysis
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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