Minimum description length methods of medium-scale simultaneous inference
Bibliographic record
Abstract
AbstractNonparametricstatisticalmethodsdevelopedforanalyzingdataforhighnumbersofgenes,SNPs,orotherbiologicalfeatures tend to overfit data with smaller numbers of features such as proteins, metabolites, or, when expression ismeasured with conventional instruments, genes. For this medium-scale inference problem, the minimum descriptionlength(MDL)frameworkquantifiestheamountofinformationinthedatasupportinganulloralternativehypothesisfor each feature in terms of parametric model selection. Two new MDL techniques are proposed. First, using teststatistics that are highly informative about the parameter of interest, the data are reduced to a single statistic perfeature. This simplifying step is already implicit in conventional hypothesis testing and has been found effective inempirical Bayes applications to genomics data. Second, the codelength difference between the alternative and nullhypotheses of any given feature can take advantage of information in the measurements from all other features byusingthosemeasurementstofindtheoverallcodeofminimumlengthsummedoverthosefeatures. Thetechniquesareapplied to protein abundance data, demonstrating that a computationally efficient approximation that is close for asufficientlylargenumberoffeaturesworkswellevenwhenthenumberoffeaturesisaslowas20. Moregenerally,theMDL-basedinformationfordiscriminationdoesnotsufferfromtheasymmetryofthep-valueasameasureofevidenceforonehypothesisoveranother.
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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 | 0.001 |
| 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) | 0.009 | 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".