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Record W17603131 · doi:10.1123/jab.23.2.119

Long spaced seeds for finding similarities between biological sequences.

2007· article· en· W17603131 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

VenueBIOCOMP · 2007
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsWestern University
Fundersnot available
KeywordsSensitivity (control systems)Homology (biology)Measure (data warehouse)MathematicsAlgorithmFraction (chemistry)Computer scienceBiologyData miningGeneticsGene

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of insoles and additional shock absorption foam on the cushioning properties of various sport shoes with an impact testing method. Three commercial sport shoes were used in this study, and shock absorption foam (TPE5020; Vers Tech Science Co. Ltd., Taiwan) with 2-mm thickness was placed below the insole in the heel region for each shoe. Eight total impacts with potential energy ranged from 1.82 to 6.08 J were performed onto the heel region of the shoe. The order of testing conditions was first without insole, then with insole, and finally interposing the shock absorption foam for each shoe. Peak deceleration of the striker was measured with an accelerometer attached to the striker during impact. The results of this study seemed to show that the insole or additional shock absorption foam could perform its shock absorption effect well for the shoes with limited midsole cushioning. Further, our findings showed that insoles absorbed more, even up to 24-32% of impact energy under low impact energy. It seemed to indicate that insoles play a more important role in cushioning properties of sport shoes under a low impact energy condition.

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: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.079
GPT teacher head0.320
Teacher spread0.240 · 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