Effect of package weight on the total amount of die stone used annually in a dental school
Bibliographic record
Abstract
PURPOSE: This study compared the total amount of Type IV dental stone (die stone) used annually by dental students when they were supplied with either 70-g or 140-g preweighed packages of die stone. MATERIALS AND METHODS: In September 1994, all the bulk containers of die stone were removed from the student dental clinic and laboratories and replaced by 70-g preweighed packages of die stone (Silky-Rock; Whip Mix, Louisville, KY). At the end of August 1998, all 70-g packages of die stone were removed from the student dental clinic and laboratories. The average annual number of 70-g packages of preweighed die stone used by the students from 1994 to 1998 was calculated. From September 1998 to the end of August 1999, only 140-g preweighed packages of die stone (Silky-Rock) were supplied to the students. The number of the 140-g packages of preweighed die stone and total weight used by students during this period were calculated and compared with the average annual number of 70-g preweighed packages of die stone used during the 1994-1998 period. RESULTS: From September 1994 to August 1998, the students used an annual average of 4060 +/- SD 250 of the 70-g packages (284.2 +/- 17.5 kg per annum). From September 1998 to August 1999, the students used 3360 of the 140-g packages (470.4 kg). CONCLUSIONS: Although the students used in total 700 fewer of the 140-g die stone packages per annum than the annual average number of 70-g packages, they used 186.2 kg (65.5%) more die stone when supplied with the 140-g packages.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".