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Record W4252545233 · doi:10.4194/1303-2712-v16_2_22

[no title]

2016· article· en· W4252545233 on OpenAlexaff
Liu Bo, Jinjuan Wan, GE Xian-ping, Xie Jun, Linghong Miao, Liangkun Pan

Bibliographic record

VenueTurkish Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsMinistry of Agriculture
FundersSpecial Fund for Agro-scientific Research in the Public InterestChinese Academy of Fishery SciencesGovernment of Jiangsu Province
KeywordsBiology

Abstract

fetched live from OpenAlex

We evaluated the effect of vitamin C (Vit C) supplementation on the resistance of Megalobrama amblycephala to Aeromonas hydrophi1a infection under pH stress. Fish were randomly divided into six groups: a control group (fed with a basal diet) and five treatment groups (fed with basal diet supplemented with 33.4, 65.8, 133.7, 251.5 and 501.5 mg/kg Vit C, respectively). After 90 days, fish were exposed to combined stressors, first pH 9.5 followed by A. hydrophila infection. The results showed that 133.7 mg/kg vit C improved complement 4 (C4), anti-superoxide anion free aradical (ASAFER) and heat shock protein (HSP) 70 compared with the control group; and 251.5 mg/kg vit C enhanced complement 3 (C3), HSP60, HSP70 and HSP90 compared to the control group before stress. After pH stress and A. hydrophila infection, hemoglobin, ASAFER and HSP60, HSP70, HSP90 in the groups fed with 133.7 and 251.5 mg/kg vit C were still significantly higher, while serum cortisol in the group fed with 251.5 mg/kg vit C was lower compared to the control group 15 days after pH stress. The cumulative mortality of the control group was higher than that of the five treatment groups at 12, 24 h after A. hydrophila infection. The results of this study suggest that 133.7-251.5mg/kg vit C in a diet could have potential to stimulate immune response, and enhance resistance against high pH stress and A. hydrophila infection of Wuchang bream.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTurkish Journal of Fisheries and Aquatic SciencesSame topicAquaculture disease management and microbiotaFrench-language works237,207