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Record W2784745071 · doi:10.18331/sfs2018.4.2.5

Mussel reefs in sub-littoral zone-An important habitat for infaunal and ichthyofaunal diversity

2018· article· en· W2784745071 on OpenAlexvenueno aff
D. Viswambharan, Geetha Sasikumar, P Rohit

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReefLittoral zoneFisheryEcologyHabitatInvertebrateFaunaMusselCoral reefGeographyBiology

Abstract

fetched live from OpenAlex

A Sub-littoral mussel reefs harbours rich and diverse invertebrate communities. They
\nutilize the reef complex as their habitat, rich feeding substrate and also as refuge from
\npredation. Though the invertebrate diversity of the reefs is available, reports pertaining
\nto their relation to dietary habit of reef fishes are lacking. A study was taken up at the
\nsub-littoral mussel reefs occurring off Someshwara Coast (120 47’ 19” N 740 51’ 05”E)
\nin Karnataka (eastern Arabian Sea) to ascertain the diversity reef as well as the fish
\nfauna of the region. The invertebrate community of the reef was collected by quadrant
\nsampling method. The details on fish fauna of the reefs were collected by visual census
\nand also by using semi-structured interviews with local fishers. Detailed study was
\ncarried out to find the dietary relationship of the ichthyofauna with the diverse
\norganism associated with the sub littoral mussel beds. Apart from barnacles and
\nmussels, the invertebrate community was dominated by polychaetes followed by
\namphipods and crablets. The major ichthyofaunal diversity includes fishes of the family
\nLeiognathidae, Lutjanidae, Siganidae, Sciaenidae, Epinephelidae, Carangidae, Ariidae,
\nHaemulidae, Cynoglossidae, and others. The information pertaining to the dietary
\nhabits of the fish assemblages were compared with the in-faunal and ichthyofaunal
\ndiversity of the reefs to bring out the importance of mussel bed habitat.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.184
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.164
GPT teacher head0.268
Teacher spread0.103 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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