NBA: Ja Morant inspires Memphis Grizzlies win while Stephen Curry stars in Golden State Warriors loss
Bibliographic record
Abstract
Ja Morant scored a game-high 49 points as the Memphis Grizzlies won 129-122 at the Houston Rockets on Friday.\n\nMemphis trailed by 16 in the second quarter before taking the lead in the third, during which Morant scored 19 points, making eight of his nine shots.\n\nHe added a game-high eight assists and blocked two shots in the fourth quarter as Houston kept things close.\n\nMeanwhile, defending champions Golden State Warriors had their first loss of the season against the Denver Nuggets.\n\nLast season's Most Valuable Player Nikola Jokic scored 26 points as part of a triple-double as Denver rebounded from their opening-game loss to beat the Warriors 128-123.\n\nNBA Finals MVP Stephen Curry put up a game-high 34 points for the Warriors, who beat the Nuggets in the first round of last season's Western Conference play-offs.\n\n Four key talking points as new NBA season begins\n Lakers continue losing start with defeat by Clippers\n\nKevin Durant hit a tiebreaking three-pointer with 56 seconds left as the Brooklyn Nets held on for a 109-105 win over the Toronto Raptors.\n\nThe Nets, who had a 22-point loss to the New Orleans Pelicans in their season opener, almost let the game slip as they led 100-88 with five minutes remaining.\n\nDurant finished with 27 points but Kyrie Irving led the Nets with 30 points and added seven assists.\n\nFriday's other games also saw Trae Young score 23 of his 25 points in the second half and finish with 13 assists to lift the Atlanta Hawks to a 108-98 win at home to the Orlando Magic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.261 | 0.091 |
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".