Bibliographic record
Abstract
The Pittsburgh Pirates have one of the lowest baseball payrolls, with the management being given only a $15 million budget in 2013 to bring in more baseball talent.Between the years 2007 and 2008, The Pittsburgh Pirates made about $29.4 million which is recorded in the financial statement documents.Even though some clubs disagree vehemently on the fact, it has become evident that Pittsburgh has spent relatively less time compared to its opponents and also evidently in the won-loss records posted.The trend shows that the team has been gaining regardless of the losses that it has been recording which still raises the question of 'does the team lose to gain more revenue' and in case the team decided to be winning, would it gain more or it would lose its consistent track of increasing trend of the revenue.Pittsburgh Pirates have utilized a skill that hasn't been used to measure the capability of players before -the Pirates used pitch-framing data to identify catchers with this skill, Framing refers to the method in which the pitched ball is received from the pitcher for presenting the pitch to the umpire in an increasing likelihood manner; hence the pitch ball being called a strike.Some of the top catchers of the team include Ralph Kiner, Honus Wagner, Willie Stargell, and Pie Traynor.The team is known to have low payments to the players and employees to save more money.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".