食品焙煎機オペレーター
食品調理・サービスAI露出度
- データ出典: BLS公表時点: 2026-08
低い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.000
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.15
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは92%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
露出のある作業のみValues in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime.
| Task | βE1 + 0.5 × E2 |
|---|---|
Record production data, such as weight and amount of product processed, type of product, and time and temperature of processing.O*NET Task ID 10063 | 1.0 |
Observe, feel, taste, or otherwise examine products during and after processing to ensure conformance to standards.O*NET Task ID 10058 | 0.0 |
Observe temperature, humidity, pressure gauges, and product samples and adjust controls, such as thermostats and valves, to maintain prescribed operating conditions for specific stages.O*NET Task ID 10059 | 0.0 |
Operate or tend equipment that roasts, bakes, dries, or cures food items such as cocoa and coffee beans, grains, nuts, and bakery products.O*NET Task ID 10060 | 0.0 |
Set temperature and time controls, light ovens, burners, driers, or roasters, and start equipment, such as conveyors, cylinders, blowers, driers, or pumps.O*NET Task ID 10061 | 0.0 |
Observe flow of materials and listen for machine malfunctions, such as jamming or spillage, and notify supervisors if corrective actions fail.O*NET Task ID 10062 | 0.0 |
Weigh or measure products, using scale hoppers or scale conveyors.O*NET Task ID 10064 | 0.0 |
Read work orders to determine quantities and types of products to be baked, dried, or roasted.O*NET Task ID 10065 | 0.0 |
Take product samples during or after processing for laboratory analyses.O*NET Task ID 10066 | 0.0 |
Fill or remove product from trays, carts, hoppers, or equipment, using scoops, peels, or shovels, or by hand.O*NET Task ID 10067 | 0.0 |
Open valves, gates, or chutes or use shovels to load or remove products from ovens or other equipment.O*NET Task ID 10068 | 0.0 |
Clean equipment with steam, hot water, and hoses.O*NET Task ID 10069 | 0.0 |
Clear or dislodge blockages in bins, screens, or other equipment, using poles, brushes, or mallets.O*NET Task ID 10070 | 0.0 |
Push racks or carts to transfer products to storage, cooling stations, or the next stage of processing.O*NET Task ID 10071 | 0.0 |
Start conveyors to move roasted grain to cooling pans and agitate grain with rakes as blowers force air through perforated bottoms of pans.O*NET Task ID 10072 | 0.0 |
Smooth out products in bins, pans, trays, or conveyors, using rakes or shovels.O*NET Task ID 10073 | 0.0 |
Install equipment, such as spray units, cutting blades, or screens, using hand tools.O*NET Task ID 10074 | 0.0 |
Test products for moisture content, using moisture meters.O*NET Task ID 10075 | 0.0 |
Signal coworkers to synchronize flow of materials.O*NET Task ID 10076 | 0.0 |
Dump sugar dust from collectors into melting tanks and add water to reclaim sugar lost during processing.O*NET Task ID 10077 | 0.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page