PROBABILISTIC APPROACH TO SELECTING A REASONABLE MINIMUM SAMPLE OF ROOMS FOR ASTM E-336 TESTING
Bibliographic record
Abstract
Faced with the potential of assessing compliance of 10,000 rooms within a new building complex, a statistical approach was developed to minimize the number of partitions to be ASTM E-336 tested. The building was constructed with various wall STCs ranging from STC 45 through STC 65+. Testing was designed primarily for wall constructions of STC 50 and STC 55 as they dominate the number of construction types. In addition to assessing compliance, the testing program was devised to find chronic deficiencies in the wall constructions during the build to result in immediate improvements in construction practices. Partitions to be tested were selected to include samples of different partition constructions, configurations, and sizes, as well as different room types, and were selected as randomly as possible spread evenly throughout the complex. The random element in selecting partitions is essential to assess workmanship of different construction crews, and is also important for maintaining the integrity of the statistical prediction process discussed below. A probability theory method was used to evaluate the results of field-measured ASTC versus the design STC requirements. This method includes a technique for assessing the results of remediating partitions that initially fail the criterion described above, and the subsequent remediation of similar partitions and adjustment of construction practices going forward. The statistical approach is designed to quantify the fraction of partitions that are rated a “Pass” for each round of testing and to compare the results to previous testing samples. The statistical analysis then provides a means of estimating the overall expected “Pass/Fail” rate for all partitions within the complex. The goal was to have a target % of total partitions to achieve the targeted ASTC rating. This level of performance is also achieved with a specific level of statistical confidence certainty. Testing of the complex was completed through an iterative process using the above approach until the set criteria were met.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".