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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".