Bibliographic record
Abstract
functions and action-execution functions. The set of histories of the system are then restricted by imposing conditions over the state variables that represent functions of the PEA Model. For example a history H is well-defined for a state S characterized by a system predicate P i Vt C ST: 3i N: H.S(t, i) 4ez HsatP@t In the analysis of a system we are only concerned with those histories that are well defined, for the identified events, states and actions. Also that the uniqueness and ordering properties are satisfied by all event functions, and the boundaries of the states and actions are marked by the corresponding start and finish events. (When we model a system we do not explicitly introduce the start and finish events for actions/states.) 26 [18] D. L. Parhas and J. Madey. Functional Documentation for Computer Systems Engineering (Version 2). Technical Report CRL Report No. 237, TRIO, McMaster University, Hamilton, Ontario, 1991. [19] A. Pnueli. Specification and Development o
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.029 |
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".