ADR1D-EWS is a reproducible machine-learning system for predicting whether contaminant concentration at a protected downstream sensor in the ADR1D benchmark will reach 0.01 mg/L within one hour. The system uses 52 causal features derived only from upstream observations available through each decision time. A histogram gradient boosting classifier and its probability threshold were selected on scenario-independent validation data and then evaluated once on a locked test partition. The release includes source sensor histories, the derived decision table, a serialized model, inference and validation interfaces, machine-readable evidence, and diagnostic figures.
Journal-facing reproducibility repository containing code, locked configurations and validation splits, raw and processed datasets, consolidated model outputs, statistical analyses, tables, figure source data, and final figures for the associated article.
This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…
Age-gated applications need accurate, privacy-preserving age checks that run on-device, yet open-source age estimators remain too coarse for the adolescent band where false accepts matter most. We present Maestro (Machine-learning Age Estimator: Smart, Trustworthy, Responsive, On…
It provides (1) a harmonized multi-device soil-erosion database — 1,146 specimen records from EFA, SETD, JET, HET and related devices (1,013 with critical shear stress and 972 with erodibility coefficient), 186 raw erosion-rate-versus-shear-stress curves with power-law fits, and…
Code and derived results for a study of whether the chemical transformations thatgenerate activity cliffs on one protein kinase predict cliffs on another. Matched molecular pairs were constructed from measured Ki and Kd binding affinitiesretrieved from ChEMBL (release 37) for 20…
Version 0.3.0 provides the sanitized, hermetic code and frozen result records supporting the chemistry-stratified selective-abstention analysis. It includes the 32-UIP roster analysis, prototype-blocked confidence intervals, mechanism and sensitivity controls, manuscript sources,…