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Record W2900016389 · doi:10.14203/mri.v17i0.350

THE PROBLEMS OF CONSERVATION OF CORAL REEFS IN NORTHWEST SABAH

2018· article· en· W2900016389 on OpenAlexaff
Nigel Langham, J. A. Mathias

Bibliographic record

VenueMarine Research in Indonesia · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of British Columbia
FundersDirectorate for Biological SciencesUniversiti Sains Malaysia
KeywordsFisheryReefCoral reefCoral reef protectionEnvironmental issues with coral reefsAquaculture of coralCoralGeographyCoral reef organizationsFaunaEcologyBiology

Abstract

fetched live from OpenAlex

In March 1974 a survey was made of the coral reefs of NW Sabah centered on three main areas (1) Kota Kinabalu, (2) Kudat and (3) Labuan. At various sites within these areas, the coral reefs were assessed according to the extent of living coral, the damage resulting from fish blasting, mining and sedimentation, and the accessibility for tourism.The coral reefs in this region support a significant fishery accounting for about 30 percent of the fish landings both in weight and monetary value. Reef fish are caught by lines, gill nets, and illegal use of explosives. The latter method has seriously damaged a number of reef habitats resulting in a marked drop in the fauna including valuable fish and invertebrates.Coral mining for limestone used for foundations of buildings and roads has been carried out on accessible reefs near Labuan and Kota Kinabalu. The removal of coral heads has resulted in extensive reef damage especially near Labuan.Recent efforts have been made to preserve these reefs and has led to the establishment of a national park around Pulau.Gaya. However, a number of other areas require protection both to safeguard the fishery and promote tourism.

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)0.002
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 score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.317
Teacher spread0.263 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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