Determining a Relationship Between Applied Occlusal Load and Articulating Paper Mark Area
Bibliographic record
Abstract
Articulating paper mark size has been widely accepted in the dental community to be descriptive of occlusal load. The objective of this study is to determine if any direct relationship exists between articulating paper mark area and applied occlusal load. A uniaxial testing machine repeatedly applied a compressive load, beginning at 25N and incrementally continuing up to 450N, to a pair of epoxy dental casts with articulating paper interposed. The resultant paper markings (n = 600) were photographed, and analyzed the mark area using a photographic image analysis and sketching program. A two-tailed Student's t-test for unequal variances compared the measured size of the mark area between twelve different teeth (p < 0.05). Graphical interpretation of the data indicated that the mark area increased non-linearly with increasing load. When the data was grouped to compare consistency of the mark area between teeth, a high variability of mark area was observed between different teeth at the same applied load. The Student's t-test found significant differences in the size of the mark area approximately 80% of the time. No direct relationship between paper mark area and applied load could be found, although the trend showed increasing mark area with elevating load. When selecting teeth to adjust, an operator should not assume the size of paper markings, accurately describing the markings' occlusal contact force content.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".