Bibliographic record
Abstract
In this paper we show how a number of different formulations of nonmonotonic reasoning, probabilistic reasoning and design can be combined into a coherent logic-based abductive framework. This framework is based on allowing consistent assumptions to be used to prove a goal. Different frameworks are characterised by who chooses the assumptions, whether an adversary chooses the assumptions, nature chooses the assumptions, or one gets to choose whatever assumptions one likes. 1 Introduction In artificial intelligence over the last decade there has been much work in logic-based nonmonotonic, probabilistic and abductive reasoning (see e.g., papers in [17, 44, 26]). In this paper, we show how a particular simple form of abductive reasoning can gives a unifying theme to many seeming disparate reasoning schemes, for example Circumscription [24] and Bayesian This paper is to appear in P. O'Rorke (Ed.) Abductive Reasoning, MIT Press, 1994. y Scholar, Canadian Institute for Advanced Researc...
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".