[ESPN-TV] Raptors vs Thunder: Live Stream (NBA Basketball Online 2019)
Bibliographic record
Abstract
The Toronto Raptors have been building nicely with four wins in their last six games and they will be feeling pretty confident after making light work of the lowly Knicks in a 128-92 home win on Monday. Jeremy Lin led the team in scoring with 20 points off the bench, =========================== Watch Live NOW >> https://247itv.info/nba-allaccess Watch Live NOW >> https://247itv.info/nba-allaccess =========================== Pascal Siakam added 19 points with eight rebounds and five assists while Kyle Lowry chipped in with 15 points and eight assists. As a team, the Raptors shot a healthy 55 percent from the field and 17 of 40 from the 3-point line as they set the tone with 38 points in the first quarter before blowing it out in the third quarter where they outscored the Knicks by 28-15. With the easy win, Toronto improved to 50-21 on the season and 29-7 at home which is good for second place in the Eastern Conference standings. Meanwhile, the Oklahoma City Thunder have been in a bit of a rut as they’ve lost four of their last six games and they will be hoping to snap a three-game skid after falling short against the Heat in a 116-107 home loss on Monday. Paul George led the team with 31 points, nine rebounds and five assists, Jerami Grant added 27 points with 10 rebounds while Dennis Schroder added 20 points with six assists in a rare start. As a team, the Thunder shot 43 percent from the field and 10 of 37 from the 3-point line but the difference between the teams was the bench scoring as the Thunder’s reserves were outscored by 67-10. Russell Westbrook sat out the game as he was serving his suspension for receiving his 16th technical foul. With the loss, Oklahoma City fell to 42-29 on the season which is good for sixth place in the Western standings. Looking at the betting trends, the Raptors are 6-14 ATS in their last 20 road games, 1-5 ATS in their last six games against a team with a winning record and 1-9 ATS in their last 10 games following a win. The Thunder are 3-7 ATS in their last 10 games against a team with a winning record, 1-6 ATS in their last seven home games and 3-12 ATS in their last 15 games overall. Head to head, the road team is 6-1 ATS in the last seven meetings, the Raptors are 4-1 ATS in the last five meetings in Oklahoma City but the Raptors are just 1-4 ATS in the last five meetings overall. The Raptors have been on a nice run lately and they will be full of confidence after destroying the hapless Knicks in their last outing, so they’ll want to keep that momentum going with a statement win over the Thunder who have been in a slump lately. Kawhi Leonard will likely return to the court after being rested on Monday, but the Raptors have a concern at the point guard position as Lowry is questionable with an ankle injury. I’m not surprised that the Thunder are still favorites at home despite their form, but I’m sticking with the Raptors and the points in what should be a close one.
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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 | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.824 | 0.706 |
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".