Bibliographic record
Abstract
Linguistics knowledge representation and its relation to context abstraction are presented in brief.Nourani (e.g.Nourani 1999a) has put forth new visual computing techniques for intelligent multimedia context abstraction with linguistics components.In the present paper we also instantiate proof tree leaves with free Skolemized trees.Thus virtual trees, at times like intelligent trees, are substituted for the leaves.By a virtual tree we mean a term made up of constant symbols and named but not always prespecified Skolem function terms.In virtual planning with generic diagrams that part of the plan that involves free Skolemized trees is carried along with the proof tree for a plan goal.We can apply predictive model diagrams to compute queries and discover data knowledge from observed data and visual object images keyed with diagram functions.Model-based computing can be applied to automated data and knowledge engineering with keyed diagrams.Specific computations can be carried out with predictive model diagrams.For cognition, planning, and learning the robot's mind, a diagram grid can define state.The starting space applicable project was meant for an autonomous robots space journeys.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".