食品調理機オペレーター
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
| Task | Claude.aiRaw / share % |
|---|---|
Measure or weigh ingredients, using scales or measuring containers.51-3093 | 0.00000.0 |
| Not observed on any surface — 16 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | |
Record production and test data, such as processing steps, temperature and steam readings, cooking time, batches processed, and test results. | —0 |
Listen for malfunction alarms, and shut down equipment and notify supervisors when necessary. | —0 |
Collect and examine product samples during production to test them for quality, color, content, consistency, viscosity, acidity, or specific gravity. | —0 |
Observe gauges, dials, and product characteristics, and adjust controls to maintain appropriate temperature, pressure, and flow of ingredients. | —0 |
Read work orders, recipes, or formulas to determine cooking times and temperatures, and ingredient specifications. | —0 |
Clean, wash, and sterilize equipment and cooking area, using water hoses, cleaning or sterilizing solutions, or rinses. | —0 |
Set temperature, pressure, and time controls, and start conveyers, machines, or pumps. | —0 |
Tend or operate and control equipment, such as kettles, cookers, vats and tanks, and boilers, to cook ingredients or prepare products for further processing. | —0 |
Admit required amounts of water, steam, cooking oils, or compressed air into equipment, such as by opening water valves to cool mixtures to the desired consistency. | —0 |
Remove cooked material or products from equipment. | —0 |
Notify or signal other workers to operate equipment or when processing is complete. | —0 |
Turn valves or start pumps to add ingredients or drain products from equipment and to transfer products for storage, cooling, or further processing. | —0 |
Pour, dump, or load prescribed quantities of ingredients or products into cooking equipment, manually or using a hoist. | —0 |
Place products on conveyors or carts, and monitor product flow. | —0 |
Activate agitators and paddles to mix or stir ingredients, stopping machines when ingredients are thoroughly mixed. | —0 |
Operate auxiliary machines and equipment, such as grinders, canners, and molding presses, to prepare or further process products. | —0 |
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. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Record production and test data, such as processing steps, temperature and steam readings, cooking time, batches processed, and test results.O*NET Task ID 4944 | 1.0 |
Read work orders, recipes, or formulas to determine cooking times and temperatures, and ingredient specifications.O*NET Task ID 4948 | 1.0 |
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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page