Datasets:

ClarusC64
/

clinical-glucose-regulation-instability-v0.1

scenario_id string	glucose_t0 int64	glucose_t1 int64	glucose_t2 int64	insulin_response_proxy_t0 float64	insulin_response_proxy_t1 float64	insulin_response_proxy_t2 float64	hepatic_buffer_proxy_t0 float64	hepatic_buffer_proxy_t1 float64	hepatic_buffer_proxy_t2 float64	metabolic_demand_proxy float64	ketone_proxy_t0 float64	ketone_proxy_t1 float64	ketone_proxy_t2 float64	intervention_delay int64	lab_noise float64	chart_noise float64	label int64
GR001	92	94	95	0.72	0.73	0.74	0.7	0.71	0.72	0.48	0.18	0.19	0.2	1	0.31	0.4	0
GR002	98	120	148	0.7	0.58	0.44	0.68	0.54	0.38	0.78	0.2	0.36	0.62	4	0.33	0.42	1
GR003	90	92	93	0.74	0.75	0.76	0.72	0.73	0.74	0.46	0.17	0.18	0.19	1	0.28	0.36	0
GR004	100	126	154	0.68	0.54	0.4	0.66	0.5	0.36	0.82	0.22	0.4	0.7	4	0.35	0.43	1
GR005	93	95	96	0.72	0.73	0.74	0.7	0.71	0.72	0.48	0.18	0.19	0.2	1	0.3	0.38	0
GR006	104	132	168	0.66	0.5	0.36	0.64	0.46	0.32	0.86	0.24	0.45	0.78	4	0.37	0.44	1
GR007	89	90	91	0.75	0.76	0.77	0.73	0.74	0.75	0.44	0.16	0.17	0.18	1	0.27	0.35	0
GR008	96	118	146	0.69	0.56	0.42	0.67	0.51	0.37	0.8	0.21	0.38	0.66	3	0.34	0.41	1
GR009	92	94	95	0.72	0.73	0.74	0.7	0.71	0.72	0.48	0.18	0.19	0.2	1	0.29	0.37	0
GR010	108	138	174	0.64	0.48	0.34	0.62	0.44	0.3	0.88	0.26	0.48	0.82	4	0.36	0.42	1
GR011	90	92	93	0.74	0.75	0.76	0.72	0.73	0.74	0.46	0.17	0.18	0.19	1	0.28	0.36	0
GR012	112	144	182	0.62	0.46	0.32	0.6	0.42	0.28	0.9	0.28	0.52	0.86	4	0.37	0.44	1
GR013	93	95	96	0.72	0.73	0.74	0.7	0.71	0.72	0.48	0.18	0.19	0.2	1	0.3	0.38	0
GR014	100	126	154	0.68	0.54	0.4	0.66	0.5	0.36	0.82	0.22	0.4	0.7	3	0.34	0.41	1
GR015	89	90	91	0.75	0.76	0.77	0.73	0.74	0.75	0.44	0.16	0.17	0.18	1	0.27	0.35	0

clinical-glucose-regulation-instability-v0.1

This dataset evaluates whether models can detect instability in glucose regulation.

Each row represents a simplified glucose control scenario observed across three time points.

The task is to determine whether metabolic glucose regulation remains stable or is moving toward regulatory instability.

Glucose stability depends on feedback between insulin signaling, hepatic buffering, and metabolic demand.

Signals that interact include:

Instability emerges when glucose rises while insulin response and hepatic buffering fail to stabilize metabolic demand.

label = 1 → glucose regulation instability
label = 0 → stable metabolic glucose control

Each row includes:

Decoy variables:

Predictions must follow:

scenario_id,prediction

Example:

GR101,0
GR102,1

Run:

python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json

Metrics produced:

accuracy
precision
recall
f1
confusion matrix
dataset integrity diagnostics

This dataset reflects latent stability geometry through observable proxies.

The generator and latent rule structure are not included.

This dataset is part of the Clarus Stability Reasoning Benchmark.

MIT