Bibliographic record
Abstract
Notice of Withdrawal: “Comparison of fit accuracy between Procera custom abutments and three implant systems” by Tiago de Morais Alves da Cunha, Roberto Paulo Correia de Araújo, Paulo Vicente Barbosa da Rocha and Rosa Maria Pazos Amoedo The above article from Clinical Implant Dentistry and Related Research , published online on 26 October 2010 in Wiley Online Library ( wileyonlinelibrary.com ), has been withdrawn by agreement between the authors, the journal's editors‐in‐chief, William Becker and Lars Sennerby, and Wiley Periodicals, Inc. This action has been agreed due to an error at the publishers that caused a duplicate of the article to be published on 22 December 2010. The correct version of the article is to be found at: “Comparison of Fit Accuracy between Procera ® Custom Abutments and Three Implant Systems” by Tiago de Morais Alves da Cunha, Roberto Paulo Correia de Araújo, Paulo Vicente Barbosa da Rocha and Rosa Maria Pazos Amoedo (doi: 10.1111/j.1708-8208.2010.00323.x ). Reference Alves da Cunha , T. d. M. , Correia de Araújo , R. P. , Barbosa da Rocha , P. V. and Pazos Amoedo , R. M. ( 2012 ), . Clinical Implant Dentistry and Related Research , 14 : 772 – 777 . doi: 10.1111/j.1708-8208.2010.00311.x
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 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 | 0.016 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.562 | 0.409 |
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".