Extension of functions and metrics with variable domains
Bibliographic record
Abstract
Let ( X , d ) be a complete, bounded, metric space. For a nonempty, closed subset A of X denote by C ⁎ ( A × A ) the set of all continuous, bounded, real-valued functions on A × A . Denote by C † = ⋃ { C ⁎ ( A × A ) | A is a nonempty closed subset of X } the set of all partial, continuous and bounded functions. We prove that there exists a linear, regular extension operator from C † endowed with the topology of convergence in the Hausdorff distance of graphs of partial functions to the space C ⁎ ( X × X ) with the topology of uniform convergence on compact sets. The constructed extension operator preserves constant functions, pseudometrics, metrics and admissible metrics. For a fixed, nonempty, closed subset A of X the restricted extension operator from C ⁎ ( A × A ) to C ⁎ ( X × X ) is continuous with respect to the topologies of pointwise convergence, uniform convergence on compact sets and uniform convergence considered on both C ⁎ ( A × A ) and C ⁎ ( X × X ) .
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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 | 0.001 | 0.001 |
| 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".