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Record W2737226624 · doi:10.5376/ija.2017.07.0010

Effect of Culture Conditions on the Levels of Serum Insulin-like Growth Factor-1 in Indian Major Carps

2017· article· en· W2737226624 on OpenAlexvenueno aff
Thangapalam Jawahar Abraham, Farhana Hoque, A. K. Das, T. S. Nagesh

Bibliographic record

VenueInternational Journal of Aquaculture · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsInsulin-like growth factorGrowth factorInternal medicineEndocrinologyBiologyMedicineReceptor

Abstract

fetched live from OpenAlex

The insulin-like growth factor-1 (IGF-1) plays an important role in the regulation of development and growth of fish. Aquaculture researchers consider it as a potential growth rate indicator. Reports on the IGF-1 of carps are limited. This study was undertaken to determine the serum IGF-1 levels of Indian major carps (IMCs) cultured in the normal pond, sewage-fed pond and captive conditions. Labeo rohita  of the normal pond recorded the highest serum IGF-1 level (2.10±0.14 ng/ml), followed by Catla catla  (1.99±0.17 ng/ml) and Cirrhinus mrigala  (1.82±0.12 ng/ml). Captive held C. catla  recorded the highest serum IGF-1 (2.10±0.19 ng/ml) compared to L. rohita  (2.04±0.08 ng/ml) and C. mrigala  (1.90±0.09 ng/ml). In the sewage-fed pond, C. catla, L. rohita  and C. mrigala  recorded the serum IGF-1 levels of 2.38±0.36 ng/ml, 2.06 ±0.03 ng/ml and 1.07±0.06 ng/ml, respectively. Significant differences existed among the serum IGF-1 levels of C. mrigala  reared in the normal and sewage-fed pond as well as the captive and sewage-fed pond (P < 0.05). It appears from the results that C. catla  and L. rohita  are the ideal cultivable species under the sewage-fed aquaculture. The serum IGF-1 level may be an effective indicator of the differences in growth of carps.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.455

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 science0.0010.000
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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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 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
Published2017
Admission routes1
Has abstractyes

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