MétaCan
Menu
Back to cohort
Record W2590122642 · doi:10.15391/snsv.2015-3.011

Control and analysis of dynamics of technical and tactical actions in defence during the game in basketball players of superleague team

2015· article· uk· W2590122642 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueСлобожанський науково-спортивний вісник · 2015
Typearticle
Languageuk
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballLeagueQuarter (Canadian coin)Control (management)Dynamics (music)Statistical analysisOperations researchEngineeringApplied psychologyPsychologyComputer scienceStatisticsMathematicsGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: to analyze the dynamics of technical and tactical actions in defense during the match in basketball major league team. Material and Methods: the methods were applied to analyze and summarize the literature, analysis of statistical reports, teacher observation of competitive activity, methods of mathematical statistics. Results: by monitoring and analyzed dynamics of technical and tactical actions in the defense during a game of basketball at the major league team. Conclusions: basketball players show the highest indicators in the third quarter and at the end of the match – in the fourth quarter there is a gradual decrease in indicators such as: the rebounds, block shots, fouls and steels, and the increase – missing the shots, ball loss.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.419
Teacher spread0.354 · 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