{
    "slots": [
        {
            "scope": "dataset",
            "slot": "nature",
            "value": {
                "nature": "observational"
            },
            "evidence": {
                "reasoning": "One row per user with self-reported or measured sleep and phone-habit metrics from a Kaggle export. Nobody here operates the process that generates these rows. Behaviors are observed, not controlled."
            }
        },
        {
            "scope": "dataset",
            "slot": "decisions_served",
            "value": {
                "decisions": [
                    "Whether to advise users to cut bedtime phone use or lower screen brightness to reduce sleep latency and sleep debt",
                    "Which bedtime app, occupation or chronotype segments to target for sleep-hygiene interventions",
                    "Whether to recommend blue light filters, given how filter use compares with sleep outcomes",
                    "Whether to set a caffeine cutoff after 5pm, based on its relationship with sleep duration and sleep quality",
                    "Which behaviors to prioritize when modeling next-day fatigue and sleep-debt category"
                ]
            },
            "evidence": {
                "reasoning": "Phone habits, caffeine, activity and brightness sit alongside outcomes (sleep hours, latency, deep and REM percentages, fatigue, debt category). Together they support comparing behavior against outcome."
            }
        },
        {
            "scope": "dataset",
            "slot": "levers",
            "value": {
                "levers": []
            },
            "evidence": {
                "reasoning": "Each row is an individual's observed behavior and outcomes. No operator controls these variables in the dataset, so the behavior columns are correlates for advice, not operator-pulled levers."
            }
        },
        {
            "scope": "dataset",
            "slot": "audience",
            "value": {
                "audience": [
                    "Sleep health researchers",
                    "Digital wellbeing product managers",
                    "Health and wellness coaches",
                    "Data analysts and data scientists building sleep or fatigue models"
                ]
            }
        },
        {
            "scope": "table:bedtime_screentime_sleep_debt",
            "slot": "role",
            "value": {
                "role": "fact"
            },
            "evidence": {
                "reasoning": "The table holds measured outcomes per user, so it is a fact table. It is also the only table, with no related dimensions, and the TABLE_TYPE of DIMENSION from the classifier does not fit the many measures."
            }
        },
        {
            "scope": "table:bedtime_screentime_sleep_debt",
            "slot": "column_bindings",
            "value": {
                "bindings": [
                    {
                        "column": "user_id",
                        "binding": "identifier"
                    },
                    {
                        "column": "age",
                        "binding": "measure"
                    },
                    {
                        "column": "gender",
                        "binding": "category"
                    },
                    {
                        "column": "occupation_type",
                        "binding": "category"
                    },
                    {
                        "column": "chronotype",
                        "binding": "category"
                    },
                    {
                        "column": "bedtime_phone_minutes",
                        "binding": "measure"
                    },
                    {
                        "column": "primary_bedtime_app",
                        "binding": "category"
                    },
                    {
                        "column": "screen_brightness_pct",
                        "binding": "measure"
                    },
                    {
                        "column": "blue_light_filter_active",
                        "binding": "flag"
                    },
                    {
                        "column": "caffeine_post_5pm_mg",
                        "binding": "measure"
                    },
                    {
                        "column": "physical_activity_min",
                        "binding": "measure"
                    },
                    {
                        "column": "sleep_latency_min",
                        "binding": "measure"
                    },
                    {
                        "column": "total_sleep_hours",
                        "binding": "measure"
                    },
                    {
                        "column": "deep_sleep_pct",
                        "binding": "measure"
                    },
                    {
                        "column": "rem_sleep_pct",
                        "binding": "measure"
                    },
                    {
                        "column": "morning_alarm_snoozes",
                        "binding": "measure"
                    },
                    {
                        "column": "next_day_fatigue_score",
                        "binding": "measure"
                    },
                    {
                        "column": "sleep_debt_category",
                        "binding": "category"
                    },
                    {
                        "column": "_ingestion_timestamp",
                        "binding": "temporal"
                    },
                    {
                        "column": "_batch_id",
                        "binding": "category"
                    },
                    {
                        "column": "_source_file",
                        "binding": "category"
                    },
                    {
                        "column": "_source_system",
                        "binding": "category"
                    }
                ]
            },
            "evidence": {
                "reasoning": "bedtime_phone_minutes is a duration in minutes, so it is a measure, not a temporal column. The detector's temporal tag came from its name, and its pii flag is a false positive for the same reason. Only the ingestion timestamp is a real time column."
            }
        },
        {
            "scope": "table:bedtime_screentime_sleep_debt",
            "slot": "temporal_posture",
            "value": {
                "posture": "snapshot",
                "columns": [
                    "_ingestion_timestamp"
                ]
            },
            "evidence": {
                "reasoning": "There is no event date. Each user appears once, and the only timestamp is a single batch ingestion time, so the table is a cross-sectional snapshot."
            }
        },
        {
            "scope": "table:bedtime_screentime_sleep_debt",
            "slot": "derived_measures",
            "value": {
                "measures": [
                    {
                        "column": "sleep_debt_category",
                        "derived_from": [
                            "total_sleep_hours"
                        ],
                        "rule": "Categorical band of sleep debt (Optimal Recovery, Mild Deficit, Moderate Debt, Severe Sleep Debt). It appears to track total_sleep_hours, with the shortest sleep in the severe rows, but the exact thresholds are not shown in the data."
                    }
                ]
            },
            "evidence": {
                "reasoning": "In the sample rows, 3.2 to 4.06 hours maps to Severe Sleep Debt, 4.58 to Moderate Debt and 6.99 to Mild Deficit. This suggests a banding of sleep hours, but it is inferred and not confirmed."
            }
        }
    ]
}