[ESPN-TV] Raptors vs Thunder: Live Stream (NBA Basketball Online 2019)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,824 | 0,706 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».