MétaCan
Menu
Back to cohort

Micotoxinas em silagem

2022· article· en· W4210402720 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePubVet · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The silage process consists of preserving green forage. However, errors in operations during harvesting, or even at the opening of the silo, can result in colonization of fungi and production of mycotoxins. Therefore, it is important to develop strategies to mitigate the negative effects of mycotoxins in the feeding of dairy cows. The objective was to review the literature on the contamination of silage by mycotoxins, including predisposing factors for contamination and ways of prevention and mitigation. The main environmental conditions that favor mycotoxin synthesis are temperature, pH and water activity. In addition, factors linked to the operation, such as delayed harvesting, delays in sealing the silo, the density of compaction or the use of damaged seals also favor fungal growth. The control of these processes in silage aims to avoid contamination by toxinogenic fungi. However, current control strategies are not entirely effective. Some safe and relatively economical measures are the use of mycotoxin adsorbents or bacterial inoculants, which can be used to reduce the absorption of mycotoxins in the gastrointestinal tract.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.995

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.173
Teacher spread0.162 · 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