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Record W7052359399

Project FASTBALL Snel en blessurevrij leren werpen

2016· article· nl· W7052359399 on OpenAlexaff

Bibliographic record

VenueDigital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) · 2016
Typearticle
Languagenl
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsResearch methodologyData collectionWork (physics)Participant observation
DOInot available

Abstract

fetched live from OpenAlex

Met een goede pitcher die snel kan werpen kun je honkbalwedstrijden winnen. Maar hoe wordt een pitcher goed? Hiervoor is inzicht nodig in de ontwikkeling en het aanleren van de werptechniek van talentvolle pitchers. Met dit inzicht kunnen we, met behulp van technische hulpmiddelen, onze pitchers naar een hoger niveau brengen.

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)0.004
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.133
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1330.047

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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