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Record W4411095869 · doi:10.53555/sfs.v6i1.3629

Growth Performance of Carps Cultured Under Different Experimental Conditions at Akividu, West Godavari District, Andhra Pradesh

2019· article· en· W4411095869 on OpenAlexvenueno aff
K. Premchand Premchand, G. Teja Teja, S. Chinnababu Chinnababu, I. Rukmini Sirisha, P. Yedukondala Rao

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGeographyFisheryVeterinary medicineMedicine

Abstract

fetched live from OpenAlex

The objective of the present study is to assess the influence of different fertilizers, supplementary feed and probiotics on growth performance of major carps Catla (Catla catla), Rohu (Labeo rohita), Mrigala (Cirrhinus mrigala) and Grass carp (Ctenopharyngodon idella) cultured under different treatments for a period of one year from August, 2014 to July 2015 at Akividu, West Godavari District, Andhra Pradesh, India. Four experimental ponds i.e. Control (C), Treatment-1 (T1), Treatment-2 (T2) and Treatment-3 (T3) were selected for this study. Among the four treatments, maximum average body weight of carps was recorded in T3. Among the four fish species, Catla showed the maximum average body weight (1313.4g) in treatment pond-3. The highest specific growth rate was observed in treatment-3. Overall the highest survival rate of four carps recorded in T3. The FCR varied from 1:2.70 to 1:3.28 in T2 and T3. The gross yield recorded as 3485.97, 4456.44, 6133.56 and 7311.98 kg/ha in Control, T1, T2 and T3 respectively. The physical and chemical characteristics of pond water remained within the favorable limits during culture. The lowest biomass of plankton (51.4 to 96.8mg/l) recorded in C, while the maximum plankton biomass (96.4 to 145.8 mg/l) recorded in T3.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.103
GPT teacher head0.249
Teacher spread0.146 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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