MétaCan
Menu
← Back to cohort
Record W4403608084 · doi:10.53555/sfs.v11i4.3103

Diversity And Distribution Of Molluscan Fauna From The Coastal And Mangrove Ecosystems Of Kakinada, Andhra Pradesh

2024· article· en· W4403608084 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsMangroveMangrove ecosystemFaunaEcosystemEcologyDiversity (politics)GeographyDistribution (mathematics)Environmental scienceOceanographyFisheryGeologyBiology

Abstract

fetched live from OpenAlex

In the present study conducted along the Kakinada coast and its associated mangrove regions from 2013 to 2014, a total of 185 molluscan species were identified.These species were classified into three major classes: Gastropoda, Bivalvia, and Cephalopoda, which were further subdivided into 7 subclasses, 24 orders, and 69 families.Among these, the class Gastropoda exhibited the highest species richness, comprising 112 species distributed across 5 subclasses, 11 orders, and 46 families.The families Cerithidae, Nassaridae, Trochidae, Turitellidae, and Turridae were particularly dominant among the gastropods.Following closely, the class Bivalvia contained 66 species categorized into 1 subclass, 11 orders, and 21 families, with families such as Arcidae, Veneridae, Donacidae, and Pectinidae being especially prominent.The class Cephalopoda was represented by 7 species across 1 subclass, 2 orders, and 2 families in the study locations.Several bivalve and gastropod species, including Tegillarca granosa, Placuna placenta, Meretrix meretrix, Magallana bilineata, Meretrix casta, Perna viridis, Pirenella cingulata, Telescopium telescopium, Umbonium vestiarium, Volegalea cochlidium, Turritella duplicata, Murex trapa, and Tonna dolium form a dominant fishery and possess considerable commercial value.These species are crucial to local economies, supporting both the fishing industry and various related sectors.Their significance extends beyond mere economic value, as they also play vital roles in the marine ecosystem, contributing to biodiversity and ecological balance.

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 score0.040
Threshold uncertainty score0.079

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 studies0.0010.000
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 head0.076
GPT teacher head0.235
Teacher spread0.159 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries Sciences→Same topicAquatic Invertebrate Ecology and Behavior→French-language works237,207→