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Referee report. For: Human activities and persistent coral reef degradation in Gaspar Strait, Bangka Belitung Islands, Indonesia [version 1; referees: 1 approved with reservations]

2019· article· en· W4416623017 on OpenAlexfundno aff
Bert W. Hoeksema

Bibliographic record

VenueFaculty of 1000 Research Ltd · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsnot available
FundersUniversitas Bangka BelitungMcMaster University
KeywordsCoral reefEnvironmental degradationCoral reef protectionReefFishing

Abstract

fetched live from OpenAlex

Background: The aim of the study was to describe the coral reef condition in Bangka Belitung Islands, particularly from Gaspar Strait. This research location is well known for its underwater archaeological discovery and shipwreck sites. Recent increases in mining, fishing and tourism activities in the surrounding islands might have affected the condition of the coral reef. Methods: Nine islands inside the strait were visited (i.e. Langer, Kembung, Piling, Aur, Salma, Pongok, Celagen, Kelapan, and Lepar Island), and a line transect was used to observed coral reef conditions. Results: Coral cover was found to be predominantly in fair conditions (25-50%). Coral mortality index also tended to be high, which indicated that the coral reef ecosystem was in threatened conditions. Previous and recent reports also reported the same condition as found by this study. Conclusion: Degradation of the coral community in Bangka Belitung Islands is likely caused by human activities. This suggests that increasing human activities significantly affects the coral reef condition. Protection of coral reefs with sustainable management for mining activity, tourism and fishing practices are needed.

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)0.003
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.522
Threshold uncertainty score0.682

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.5220.205

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 head0.051
GPT teacher head0.313
Teacher spread0.262 · 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.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractno

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