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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".