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KARAKTERISTIK DAN DAMPAK SIKLON TROPIS YANG TUMBUH DI SEKITAR WILAYAH INDONESIA

2018· article· en· W2989613929 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOSEANA · 2018
Typearticle
Languageen
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsTropical cycloneCyclone (programming language)Extratropical cycloneClimatologyEnvironmental scienceEquatorTropical cyclone scalesLatitudeGeographyGeology

Abstract

fetched live from OpenAlex

CHARACTERISTICS AND IMPACTS OF TROPICAL CYCLONES GROWING AROUND INDONESIAN TERRITORY. Tropical cyclone is a cyclonic originates from tropical oceans and driven principally by heat transfer from the ocean. Tropical cyclone is an atmospheric phenomenon characterized by the emergence of low air pressure that triggers the occurrence of strong winds due to the process of heat transfer from the equator to the latitude. This phenomenon can not be prevented, so that it has great potential to impact on the damage in the area it through. Tropical cyclones can be characterized through their life cycle, scale of power and how it impacts in the area it through. The Cempaka and Dahlia tropical cyclone occuring in 2017 greatly influenced territory of Indonesia. The effect of the cyclone causes extreme weather in Indonesia, especially in areas close to where cyclones are formed.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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