Bibliographic record
Abstract
The objective of this research is to determine the correlation between speed of reaction (X1), agility (X2) and confidence (X3) with anticipation of the penalty kick (Y). This research was conducted with a sample of 10 respondents selected using purposive sampling. The results of this research are as follows. First, there is a positive correlation between speed of reaction toward the anticipation of the penalty kick. The linear regression is express through ? = -6.74 + 43.82X1.The correlation coefficient 0.773. It means the speed of reaction toward the anticipation the penalty kick is 60 %. Second, there is a positive correlation between agility toward the anticipation of the penalty kick. The linear regression is expressed through ? = -10.42 + 0.86X2. The correlation coefficient is 0.784. It means the agility toward the anticipation of the penalty kick is 59 %. Third, there is a positive correlation between confidence toward the anticipation of the penalty kick. The linear regression is expressed through ? = 27.73 + 0.40X3. The correlation coefficient is 0.784. It means the confidence toward the anticipation of the penalty kick is 61 %. Fourth, there is a positive correlation between speed of reaction, agility and confidence with the anticipation of the penalty kick. The linear regression is expressed through ? = -24.995 + 430.37 X1 + 0.016X2 +0.198X3. The correlation coefficient correlation is 0.9105. It’s mean the speed of reaction, agility and confidence with anticipation the penalty kick is 91 %.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".