Concerns starting to mount for Joel Embiid, struggling 76ers after 1-4 start to 2022-23 season Concerns starting to mount for Joel Embiid, struggling 76ers after 1-4 start to 2022-23 season
Bibliographic record
Abstract
When the Philadelphia 76ers dropped their first two games of the 2022-23 season to two legitimate contenders in the Boston Celtics and Milwaukee Bucks, the concern level was pretty low. It was the beginning of the season, and the Sixers were trying to incorporate several new pieces while those teams had the advantage of continuity. When the Sixers lost their third game of the season to the rebuilding San Antonio Spurs, eyebrows were raised, but the team then got the first win of the season over the upstart Indiana Pacers, and there was a sense of relief.\n\n"It felt good," Sixers guard James Harden said after the win over Indiana. "It felt like we were 0-82." \n\nThose good vibes didn't last long, though, as Philadelphia followed that win up with a 119-100 loss to the Raptors in Toronto to fall to 1-4 to start the season. Now, with a handful of games as a sample size, the concerns facing the team are becoming a bit more legitimate, albeit not dooming yet, given the fact that it's still extremely early in the season, so the team has time to figure things out. If they don't improve in the following areas in short order, the season could quickly spiral downhill for a team that entered with championship aspirations.\nBench production\n\nDespite making several additions to it over the offseason, the Sixers haven't gotten nearly enough production from their bench so far this season. In addition to prying P.J. Tucker away from Miami, the Sixers also added Danuel House, Montrezl Harrell, and De'Anthony Melton. The hope was that these additions would take Philadelphia's bench -- a problem area in recent years -- to the next level, but that hasn't been the case.\n\nThrough five games, the Sixers rank dead last in the NBA in bench points per performance with just 17.6. They're the only team not getting at least 20 points out of their reserves in the young season. The new guys have struggled with consistency while trying to figure out their respective roles on the team. In contrast, carryover players -- like Matisse Thybulle, Shake Milton, and Furkan Korkmaz -- have been largely removed from the rotation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.113 | 0.038 |
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".