On Axiomatization and Inference Complexity over a Hierarchy of Functional Dependencies.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Functional dependencies (FDs) have recently been extended for data quality purposes with various notions of similarity instead of strict equality. We study these extensions in this paper. We begin by constructing a hierarchy of dependencies, showing which dependencies generalize others. We then focus on an extension of FDs that we call Antecedent Metric Functional Dependencies (AMFDs). An AMFD asserts that if two tuples have similar but not necessarily equal values of the antecedent attributes, then their consequent values must be equal. We present a theoretical foundation for AMFDs, including a sound and complete axiomatization as well as an inference algorithm. We compare the axiomatization of AMFDs to those of the other dependencies, and we show that while the complexity of inference for some FD extensions is quadratic or even co-NP complete, the inference problem for AMFDs remains linear, as in traditional FDs. We implemented our inference procedure and experimentally verified its efficiency.
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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 | 0.001 | 0.002 |
| 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 it