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Record W2079875228 · doi:10.17017/jfish.v2i3.2014.40

Abundance of anemone fishes in North Bay Island and mass culture of live food organisms for their larval rearing

2014· article· en· W2079875228 on OpenAlexaboutno aff
R. Rajaram, Selvaraj Ramesh, Shimul Roy, M.A.Badhul Haq, Vaikundamoorthy Ramalingam

Bibliographic record

VenueJournal of Fisheries · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsBayZooplanktonAnemoneTransectBiologyAbundance (ecology)FisheryIchthyoplanktonPhytoplanktonEcologyLarvaOceanographyNutrient

Abstract

fetched live from OpenAlex

Understanding the transect survey for abundance of anemone fishes and other living organisms is important to asses reef associated fish diversity in North Bay island. The percentage distribution of 10 different substratum from the disturbed, semi-disturbed and undisturbed areas was recorded during the survey in North Bay islands during November 2009 to April 2010. The survey observations reveal that the fishes were the dominant groups followed by mollusks, lobsters and octopus. There are 5 different anemone fishes were collected during the transect survey and their distribution is more in undisturbed area. We are standardizing the different mass culture techniques for production of phytoplankton and zooplankton for the nutritional source for the anemone fish larvae. Monitoring the water quality parameters and culture the phytoplankton and zooplankton used in different culture media with 2 adjustment studies like with and without salinity adjustment. The results of this experiment indicate that zooplankton was rich in protein and fat content and it will be used as high nutritional source for feeding fish larvae.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.204
Teacher spread0.194 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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