WARREN, D.H.D. 1982b. "Perpetual Processes -- An Unexploited Prolog Technique", Short Communication,
Bibliographic record
Abstract
)", Tech. Report, Computer Science Dept., Univ. of Victoria, Victoria BC, Canada. WARREN, D.H.D. 1982a. "Higher-order extensions to Prolog: are they needed?", In Machine Intelligence 10, J. Hayes, D. Michie, and Y.-H. Pao (eds.), Ellis Horwood, Chicester, pp. 441-453. NGUYEN, V., DEMERS, A., GRIES, D., AND OWICKI, S. 1986. "A Model and Temporal Proof System for Networks of Processes", Distributed Computing, No. 1, pp.7-25. OHKI, M., TAKEUCHI, A., AND FURUKAWA, K. 1988. "An ObjectOriented Programming Language based on the Parallel Logic Programming Language KL1", In Logic Programming: Proc. of the 4th Int. Conf., J.L. Lassez (ed.), MIT Press, Cambridge, MA, Vol. 2, pp. 894-909. PALMER, D., AND NAISH, L. 1991. "NUA-Prolog: An Extension to the WAM for Parallel Andorra", In ICLP'91: 8th Int. Conf on LP, Paris, June. PEREIRA, F.C.N. 1990. "Prolog and Natural-Language Analysis: Into the Third Decade", Invited Lecture, In Proc. of the 1990 North American Conference on Logic Programming, S. ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 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".