MétaCan
Menu
Back to cohort
Record W6925415964 · doi:10.18712/nsd-nsd2584-2-v3

Travel and Holiday Survey 2017, 2nd quarter, Person File + Wood Firing + Health & Vaccination

2022· dataset· en· W6925415964 on OpenAlexaboutno aff

Bibliographic record

VenueNSD – Norsk senter for forskningsdata · 2022
Typedataset
Languageen
FieldComputer Science
TopicAdvanced Clustering Algorithms Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)The InternetNorwegianVaccinationWork (physics)National Health Interview Survey

Abstract

fetched live from OpenAlex

The travel and holiday survey is conducted four times a year, once every quarter. The main purpose of the survey is to establish Norwegians travel habits as well as to gather other official statistics. Apart from data about norwegians travel habits data is also collected about use of tobacco, alcohol and other substances, use of internet and informational technology, solid fuel heating, flu vaccine, vaccination of children and also attitudes towards immigration. Some subjects are included every quarter, others are only included once. This is the person file + solid fueld heating + health & vaccination. A person file + ICT and a travel file is also available for order. An overview of subjects covered in the second quarter follows below. - Household - Work and employment - Travels with atleast one overnight stop - Informational technology - Internet usage - Solid fuel heating - Health and vaccination - Income

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.062
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.047

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 head0.068
GPT teacher head0.345
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueNSD – Norsk senter for forskningsdataSame topicAdvanced Clustering Algorithms ResearchFrench-language works237,207