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Record W4389402241 · doi:10.5376/ijms.2023.13.0005

Exploring Marine Biodiversity from Concepts

2023· article· en· W4389402241 on OpenAlexvenueno aff
Jinni Wu, Qikun Huang

Bibliographic record

VenueInternational Journal of Marine Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityConvention on Biological DiversityAquatic biodiversity researchMarine biodiversityEnvironmental resource managementEnvironmental planningMarine conservationLegislationNegotiationMarine protected areaJurisdictionMeasurement of biodiversityCorporate governanceBusinessGeographyPolitical scienceEnvironmental scienceEcologyBiodiversity conservationBiology

Abstract

fetched live from OpenAlex

The ocean is the cradle of life, and caring for the ocean and protecting marine biodiversity is protecting humanity itself. In recent years, global marine governance with biodiversity as its focus has entered a period of rule reshaping. The Convention on Biological Diversity (CBD) negotiations, negotiations on biodiversity conservation and sustainable use of offshore areas beyond jurisdiction (BBNJ), international undersea regional environmental management (REMP), and the construction of Antarctic protected areas have been promoted in a coordinated manner from multiple levels of science, management, legislation, policy, and practice, showing a chain trend. Understanding the formation, basic concept classification, and influencing factors of marine biodiversity is not only an essential issue in life science, but also has important guiding significance for the protection, development, and utilization of marine biological resources. This study analyzes the formation of marine biodiversity, focusing on the concept, classification, influencing factors, etc. of marine biodiversity, and provides basic suggestions for future research on marine biodiversity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.018
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.053
GPT teacher head0.267
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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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