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Record W3090464110 · doi:10.24036/107311-0934

Kemas Ulang Informasi dalam Pembuatan Buku Pintar Siaga (Studi Kasus: Pada Kantor Badan Penanggulangan Bencana Daerah Sumatera Barat)

2019· article· en· W3090464110 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

VenueIlmu Informasi Perpustakaan dan Kearsipan · 2019
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsNatural disasterPreparednessLivelihoodHuman lifeSymbol (formal)Political scienceGeographyBusinessComputer securityComputer scienceLawArchaeology

Abstract

fetched live from OpenAlex

AbstractDisaster is an event or series of events that threatens and disrupts people's lives and livelihoods caused by natural factors and non-natural factors and human factors resulting in human casualties, environmental damage, property losses, and psychological impacts, a phenomenon of life humans who cannot be known exactly when it happened. In facing disaster preparedness in Indonesia, there is still a lack and lack of hate education so that there is a lack of public knowledge in post-disaster planning and their readiness to anticipate disasters. Therefore, repacking information is packing information back, or changing from one form of information, to a symbol that is interpreted as a message, recorded as a sign, or sent as a signal. Preparing knowledge about disaster preparedness or hate from an early age to people who are vulnerable to disasters and in preparing themselves for disasters.Keywords: information, repackaging, disaster

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.005
Open science0.0040.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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