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Record W2014820656 · doi:10.3141/1777-06

Activity Patterns of Canadian Women: Application of ClustalG Sequence Alignment Software

2001· article· en· W2014820656 on OpenAlexafffundabout
Clarke Wilson

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2001
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsCanada Mortgage and Housing Corporation
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSoftwareSequence (biology)Constraint (computer-aided design)The InternetFeature (linguistics)Computer scienceEvent (particle physics)EngineeringWorld Wide WebBiology

Abstract

fetched live from OpenAlex

Sequence alignment procedures and a new software package for creating multiple sequence alignments, called ClustalG, are described and used to identify the activity patterns of Canadian women. ClustalG eliminates the constraint on the size of the event classification system implemented in software developed for biological applications, and this feature allows an analysis of activity sequences in conjunction with their settings, in this example, location and the presence of other people. Work, weekend days, age, marital status, and the presence of children relate to membership in different activity-pattern groups. The addition of data on activity setting to the sequences changes the discriminatory power of the analysis. When the analysis omits settings, the daily activities of elderly women are grouped with weekend activities of younger, family-raising women. Inclusion of settings identifies the activities of elderly women largely on the basis of location at home and isolation from other people. ClustalG is freely available over the Internet at www.stmarys.ca/partners/iatur/clustalG.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.667

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.011
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.232
GPT teacher head0.437
Teacher spread0.205 · 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

Citations37
Published2001
Admission routes3
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicdemographic modeling and climate adaptationFrench-language works237,207