[ESPN-TV] Senators vs Flames: Live Stream (NHL Hockey Online 2019)
Bibliographic record
Abstract
The Ottawa Senators could use a win here after losing 12 of their last 15 games. The Ottawa Senators are averaging 2.9 goals per game and are scoring on 19.8 percent of their power play opportunities. Brady Tkachuk leads Ottawa with 16 goals, Thomas Chabot has 36 assists and Chris Tierney has 107 shots on goal. Defensively, the Ottawa Senators are allowing 3.7 goals per game and are killing 79.7 percent of their opponents power plays. Watch Live:: https://247itv.info/ice-hockey Watch Live:: https://247itv.info/ice-hockey Craig Anderson has given up 145 goals on 1,499 shots faced and Marcus Hogberg has allowed 14 goals on 121 shots. The Ottawa Senators have allowed three or more goals in seven of their last 10 games. The Calgary Flames look for another win after winning four of their last five games. The Calgary Flames are averaging 3.6 goals per game and are scoring on 21.3 percent of their power play opportunities. Johnny Gaudreau leads Calgary with 35 goals, Mark Giordano has 54 assists and Sean Monahan has 192 shots on goal. Defensively, the Calgary Flames are allowing 2.8 goals per game and are killing 79.9 percent of their opponents power plays. David Rittich has given up 102 goals on 1,148 shots faced and Mike Smith has allowed 98 goals on 941 shots. The Calgary Flames have allowed three or less goals in six of their last seven games. The Senators are 18-42 in their last 60 games as an underdog, 8-22 in their last 30 games playing on 0 days rest and 17-55 in their last 72 games as a road underdog. The Flames are 20-7 in their last 27 games as a home favorite, 28-9 in their last 37 games playing on 1 days rest and 35-17 in their last 52 games as a favorite. The Senators are 3-8 in the last 11 meetings in Calgary and 1-4 in the last 5 meetings. The under is 4-1 in Senators last 5 road games. The under is 8-3-3 in Flames last 14 overall. The road team has won five of the last six meetings between the Senators and Flames, and these meetings are usually competitive with them splitting the last 10 meetings overall. I wouldn't talk you out of backing the Senators here in the massive underdog role given that no hockey team should be this big of a favorite. However, the Senators continue to be awful, while the Flames have been rock solid defensively and have been nothing but money in the home favorite role. The price is outrageous and you shouldn't make a habit of laying these numbers, but this is a game the Flames should win easily.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.723 | 0.541 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".