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Study of Types and Production of Trap Net Fishery Fish based on Moon Phase in the Waters of Segeri District, Pangkep Regency, South Sulawesi Indonesian

2023· article· en· W4324357144 on OpenAlexaboutno aff
Ihsan Ihsan, Muhammad Jamal, Asbar Asbar, Herianto Suriadin, Ahmad Taufik Kafi

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsNew moonFull moonFisheryQuarter (Canadian coin)Fish <Actinopterygii>Environmental scienceGeographyOceanographyPhysicsEcologyBiologyGeologyArchaeology

Abstract

fetched live from OpenAlex

Abstract The research was carried out in October-November 2020 in the waters of the Pangkep Regency. The data collected consisted of primary and secondary data, using direct observation in the field. Tidal observations were carried out in every moon phase for 2 x 24 hours. I collected data on the type and number of catches in each month’s phase. Tidal analysis using the criminal method and descriptive analysis of the types and quantities of trap net fish production (kg) and (Rp). The full moon phase is the highest tide at 185 cm and the lowest at 42 cm. The average sea level (mean sea level) is 113.5 cm. Types of trap net catch based on the moon phase in the waters of Sergei District, Pangkep Regency, identified 13 types of catches. The highest amount of fish production was obtained by the full moon phase of 100 kg, followed by the 80 kg dead moon phase, 95 kg early quarter moon, and 65 kg final quarter moon phase.

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 score0.019
Threshold uncertainty score0.039

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 studies0.0000.000
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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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