Bibliographic record
Abstract
Rank order data are pervasive in science and in our daily lived experience. With the advent of high performance computing and the commensurate increase in available data, the opportunity to capture the overall distribution of values by means of nonparametric curve fitting enables the identification of exceptional points in large datasets. With a rank order structure, these distributions may exhibit a “knee” delineating a threshold between exceptional points and those of the baseline. Given an accurate characterization of the distribution of prediction scores, including careful identification of the knee, we have previously shown that predictive performance can be significantly improved by leveraging this “context”. This paper examines the nonparametric characterization of such distributions. Locally weighted regression (LOESS) is a widely used nonparametric approach to curve fitting. Here, we revisit the assumptions behind the selection of kernel functions for nonparametric curve fitting of biological and biomedical data exhibiting rare or exceptional instances. We propose a new linear asymmetric kernel function and compare it to the commonly used tricube kernel used in LOESS. We evaluate its ability to fit rank order data in the domain of protein-protein interaction prediction. The proposed linear kernel significantly improved predictive performance ) of two state-of-the-art predictors and promises to be widely applicable in related machine learning pipelines and nonparametric regression tasks.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".