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Record W6969224330 · doi:10.5281/zenodo.7260560

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

2022· article· en· W6969224330 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsnot available
Fundersnot available
KeywordsMountChampionshipGuard (computer science)DozenSpring (device)

Abstract

fetched live from OpenAlex

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\n"It felt good," Sixers guard James Harden said after the win over Indiana. "It felt like we were 0-82."  \n\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\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\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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.002

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.

Opus teacher head0.046
GPT teacher head0.265
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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