Operateurs de Machines de Cuisson
Préparation Alimentaire et ServiceExposition à l'IA
- Source des données: BLSPublié: 2026-08
Faible· relative
FaibleQuatre bandes relativesTrès élevéeValeur par groupe professionnel
Échelle, base et source
Quatre bandes relatives (Faible / Modérée / Élevée / Très élevée)
831 métiers détaillés du tableau des projections d'emploi du BLS. La valeur est attribuée par code de la National Employment Matrix (NEM), si bien que les métiers partageant un code NEM reçoivent la même bande
- Source des données: AnthropicPublié: 2026-03
0.000
0.000Étendue des valeurs présentées ici0.745Échelle, base et source
- Source des données: ILOPublié: 2025
0.15
0.09Étendue des valeurs présentées ici0.70Valeur par groupe professionnel
Échelle, base et source
Indice d'exposition à l'IA générative, 0–1 tel que publié
Groupe de base CITP-08 — tous les métiers partageant le code reçoivent cette valeur
Calculé par ce site, non publié par l'OIT : sur les 1 012 professions que ce site relie au jeu de données de l'OIT, 92% atteignent ou dépassent cette valeur.
Quelle est la nature de la valeur publiée par cette source
La catégorie BLS est un rang relatif et non un niveau absolu, et ce n'est pas une mesure de première main : elle regroupe en quatre bandes les rangs centiles du métier dans plusieurs études publiées. Ce n'est ni une prévision d'emploi ou de salaire, ni une probabilité d'adoption, et elle ne distingue pas automatisation et augmentation.
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