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Record W4411212248 · doi:10.2166/9781789065107_0007

Datatyper: terminologi och exempel

2025· book-chapter· sv· W4411212248 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

VenueIWA Publishing eBooks · 2025
Typebook-chapter
Languagesv
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Data som produceras på reningsverk erhålls genom en mängd olika enheter, inklusive ställdon, styrenheter och sensorer. Detta resulterar i data som kan vara mycket varierande i sin struktur. Att hantera denna blandning och heterogeniteten i dataformat kan vara en utmaning när data ska lagras eller tolkas. Av denna anledning beskriver detta kapitel de strukturella aspekterna av data på ett vanligt reningsverk. Syftet med detta kapitel är att: Introducera grundläggande begrepp för att beskriva, förstå och hantera data som produceras av onlineinstrument såsom givare och ställdon (t.ex. ventiler och pumpar).Definiera de vanligaste termerna som används i rapporten och som rör givare och andra datakällor.Illustrerar definitionerna med praktiska exempel. Där så är möjligt har vi nyttjat befintliga standarder och referenser för definitionerna även om flertalet definitioner har tagits fram specifikt för denna rapport.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0110.002
Open science0.0050.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.274
Teacher spread0.202 · 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