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Record W4384454142 · doi:10.12777/kpl.6.2.134-137

KAJIAN POTENSI PERIKANAN TANGKAP DAN PERTUMBUHAN JUMLAH KAPAL TANGKAP ( PURSE SEINE) DI KABUPATEN PEKALONGAN

2012· article· en· W4384454142 on OpenAlexaboutno aff
Bambang Sri Waluyo

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

In this paper Growing up Catching Vessel every year in District Pekalongan become CPEU Drop in every year. One of Thegreat potential of Indonesian ocean is fishing catch. Cause Indonesia have 17.502 island and have longest margine line island after Canada about 81.000 km, with convention PBB ( UNCLOS ) 1982 Indonesia have 3,1 million km2 and additional 2,7 million km2cause we have ZEEI. The condition make Indonesia have very big Ocean potention fish catching about 6,4 Million ton/year

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0330.004

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 head0.185
GPT teacher head0.465
Teacher spread0.279 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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