Exploratory Work for the PMP Heavy-Duty Inter-laboratory Correlation Exercise
Bibliographic record
Abstract
The results of the LD inter-laboratory exercise were reported during 2007 (Andersson et al. 2007).\tThe HD exercise consists of three parts:\n¿\tThe exploratory work at JRC for the definition of the measurement protocol. It includes system and sample backgrounds, filter media and filter face velocity effects, preconditioning, comparisons of different particle number systems, and investigation of the particle number instruments.\n¿\tThe validation exercise for the evaluation of particle number repeatability and reproducibility using the same measurement systems at all labs (Golden Systems). In the validation exercise, an engine (Golden Engine) will be circulated along with two Golden Systems used to simultaneously sample from full (FFDS) and partial flow exhaust dilution (PFDS) systems respectively. The Golden Engineer and project manager will ensure that the participating labs closely follow the measurement protocol. Low sulphur fuel and lubricant from single batches will be used at all labs. The participating labs are JRC (I), AVL_MTC (S), RICARDO (UK), UTAC (F), and EMPA (CH). JRC tested first and will also measure last to demonstrate consistency of measurement systems and test engine.\n¿\tThe round robin exercise for the evaluation of particle number repeatability and reproducibility using different systems. In the round robin, a reference engine will circulate, but each lab will use its own particle number systems from FFDS and optionally from PFDS. All labs will use fuel and lubricant of the same type (but not from the same batch). The participating labs are from EU, Japan, Korea and Canada. \n\tThe validation and the round robin exercises, which will run in parallel, started after the exploratory work in JRC (Feb 2008).
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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 | 0.108 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
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".