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Record W4390475184 · doi:10.5376/ijms.2023.13.0007

Exploration of Marine Biodiversity Based on Molecular Biology

2023· article· en· W4390475184 on OpenAlexvenueno aff
Wei Wang, Jinni Wu, Lingling Cai

Bibliographic record

VenueInternational Journal of Marine Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityAquatic biodiversity researchMarine biodiversityMarine biologyConservation biologyMarine conservationBiologyEnvironmental resource managementEnvironmental planningEcologyGeographyEnvironmental science

Abstract

fetched live from OpenAlex

The global sea area is vast, and in order to protect marine biodiversity and maintain the sustainable development of marine biological resources, it is necessary to deepen the understanding and exploration of marine biodiversity. The application of molecular biology technology in marine biology and biological oceanography began in the mid-1980s. As a powerful means, this technology can be applied to the analysis of marine biodiversity. In recent years, this technology has developed rapidly both domestically and internationally. This review provides an overview of various methods of molecular biology technology and analyzes its significance in exploring marine biodiversity through case studies. It is hoped that a comprehensive understanding of molecular biology technology can provide theoretical reference for marine biodiversity conservation.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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
Published2023
Admission routes1
Has abstractyes

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