Bibliographic record
Abstract
The Rugby Sevens World Series concludes with the team who accumulated the most points in the season being crowned champions. The objective of every team is to win their matches, but it is questionable whether winning every tournament is essential to winning a Series. The purpose of this study was: i) to determine the number points that must be accumulated in order to win the Series and if the sequence of point accumulation was important; and ii) to determine the performance of the 5 most successful nations between 1999/2000 and 2011/2012. Data collected from the IRB Sevens website included the Series points scored after each tournament and at the end of the season and the tier (Cup, Plate, Bowl or Shield) of the last match played for the Series winner, 2nd and 3rd placed teams and New Zealand, Fiji, South Africa, Samoa and England. Winners accumulated 83.5% (± 5.5) of the points available per season, 2nd and 3rd teams had 69.9% ± 8.1 and 61.2% ± 4.6 points respectively. New Zealand averaged 76.9% ± 13.8, Fiji 65.5% ± 12.3, South Africa 55.8% ± 12.1, England 50.8% ± 21.5, and Samoa 49.3% ± 17.5. Winning teams (and New Zealand and Fiji) accrued points faster than other teams with over 50% of points won at halfway point in the season. New Zealand had the greatest probability of winning cup quarter or final matches; Fiji was more likely to win semi-finals. No team has won every tournament during a season but a successful start is vital for a Series winner.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".