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Record W4391793843 · doi:10.53555/sfs.v10i1s.2293

Carbon-Nitrogen Ratios And Performance Of Nile Tilapia And Stinging Catfish In Biofloc Based Juvenile Rearing System.

2023· article· en· W4391793843 on OpenAlexvenueno aff
Amitesh Bhattacharyyaa, Swagat Ghosh

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsNile tilapiaCatfishJuvenileFisheryOreochromisNitrogenTilapiaBiologyAnimal scienceZoologyEnvironmental scienceFish <Actinopterygii>EcologyChemistry

Abstract

fetched live from OpenAlex

A four-month indoor culture experiment was led to assess the impact of shifting C: N ratio on production, feed utilization, and physio-chemical parameters of biofloc-based juvenile rearing systems of Nile tilapia and stinging catfish with varying carbon-nitrogen proportions (C/N). The experiment contained different C: N ratios with every three replicates of 5:1, 10:1, 15:1, and 20:1 along with control. The outcomes indicate that the C/N ratio of 15: 1 combined with minute water exchange is ideal for enhanced survival and growth. After the feeding trial, a significant increase (p<0.01) of 55.8% was observed in specific amylase activity in the C: N ratio 5: 1 in hepatopancreas, but no significant differences (p>0.05) were observed in amylase activity in the gut as compared to control. Whereas, a significant increase (p<0.01) of 84.3 and 82.3% was observed in the protease activity in the C: N ratio 5: 1 respectively in hepatopancreas and gut.

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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.230
Teacher spread0.140 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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