An Evaluation on the Lightings of Artificial Turf Football Fields Owned by Official Organizations and Private Enterprises
Bibliographic record
Abstract
The aim of this study was to investigate the lighting systems of artificial turf football fields of public and private sectors and to reveal and compare the current situation with the ideal one that it should be. 21 artificial football fields, 6 artificial turf football fields out of 9 from public sector and 15 artificial turf football fields out of 85 from privately owned organizations, have been examined in five province of Turkey. The general standards and the compliance to these standards by the present artificial turf football fields in terms of lighting of the place were studied. Data were evaluated with using descriptive analysis technic. Artificial football fields taken into consideration by researchers and experts were subjected to observation along with measurement. It was confirmed that out of 6 public and 15 private, totally out of 21 artificial turf football fields, 38% (8 of them) were below the minimum lighting level, 19% (4 of them) were at minimum lighting level and 43% (9 of them) were below average lighting level. As a result, for a competition to be played, brightness level should be at least 150 lx < E < 500 lx. It was found out that, out of 6 public sector and 15 privately owned artificial football fields, 62% was on the minimum brightness level and the other 38% was even under minimum brightness level. It was confirmed that 4 out of 6 (67%) officially owned artificial turf football fields and 9 out of 15 (60%), privately owned artificial football fields were on minimum brightness level. It was also determined that 1 out of 6 (17%) officially owned Astroturf Football Fields and 5 out of 15 (33%) privately owned Astroturf Football Fields were between minimum and average brightness levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".