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Record W26157273 · doi:10.1589/jpts.27.1609

Expert querying and redirection with rule responder

2007· article· en· W26157273 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

VenueJournal of Physical Therapy Science · 2007
Typearticle
Languageen
FieldComputer Science
TopicData Mining Algorithms and Applications
Canadian institutionsResearch and Productivity Council
Fundersnot available
KeywordsComputer scienceData mining

Abstract

fetched live from OpenAlex

[Purpose] The aim of this study was to determine the effect of cervical posture manipulation, based on passive motion analysis (MBPMA) and general mobilization, on cervical lordosis, forward head posture (FHP), and cervical ROM in university students with problems in cervical posture and range of motion (ROM). [Subjects] The Subjects were 40 university students in their 20s who displayed problems in cervical posture and ROM; they were divided into an MBPMA group (n=20) and a mobilization group (n=20). [Methods] Each group underwent MBPMA or mobilization three times a week for four weeks. The effects of MBPMA and mobilization on cervical lordosis, FHP, and cervical ROM were analyzed by radiography. [Results] MBPMA was effective in increasing the cervical lordosis, cervical extension ROM (CER), and ranges of flexion and extension motion (RFEM) and in decreasing FHP. Mobilization was effective in increasing CER and decreasing FHP. [Conclusion] MBPMA can be utilized as an effective method for decreasing FHP and improving cervical lordosis and cervical ROM.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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