खाद्य बैच मेकर
खाद्य तैयारी और सेवाAI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: 2026-08
मध्यम· सापेक्ष
कमचार सापेक्ष श्रेणियाँबहुत उच्चव्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
चार सापेक्ष बैंड (कम / मध्यम / उच्च / बहुत उच्च)
BLS रोज़गार पूर्वानुमान तालिका के 831 विस्तृत व्यवसायों के आधार पर। मान NEM (नेशनल एम्प्लॉयमेंट मैट्रिक्स) कोड के स्तर पर दिया जाता है, इसलिए एक ही NEM कोड वाले व्यवसायों को वही बैंड मिलता है
- डेटा स्रोत: Anthropicप्रकाशन: 2026-03
0.000
0.000यहाँ दिए गए मानों की सीमा0.745मापक्रम, आधार और स्रोत
- डेटा स्रोत: ILOप्रकाशन: 2025
0.15
0.09यहाँ दिए गए मानों की सीमा0.70व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
जेनरेटिव AI एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
ISCO-08 यूनिट समूह — समान कोड वाले सभी व्यवसायों को यही मान मिलता है
यह इस साइट की गणना है, ILO द्वारा प्रकाशित आँकड़ा नहीं: इस साइट द्वारा ILO डेटासेट से जोड़े गए 1,012 व्यवसायों में से 92% इस मान के बराबर या उससे अधिक हैं।
यह स्रोत किस प्रकार का आँकड़ा प्रकाशित करता है
BLS की श्रेणी सापेक्ष रैंक है, निरपेक्ष स्तर नहीं, और यह प्रथम-हस्त माप भी नहीं है: यह कई प्रकाशित अध्ययनों में व्यवसाय की पर्सेंटाइल रैंकों को चार बैंडों में बांटती है। यह रोज़गार या वेतन का पूर्वानुमान नहीं है, न अपनाए जाने की संभावना, और यह स्वचालन और संवर्धन में अंतर नहीं करती।
Task-level exposure
| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Formulate or modify recipes for specific kinds of food products.51-3092 | 0.00000.0 | —0 |
Grade food products according to government regulations or according to type, color, bouquet, and moisture content. | —0 | 0.00000.0 |
| Not observed on any surface — 23 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 for each food product batch, such as the ingredients used, temperature, test results, and time cycle. | —0 | —0 |
Observe gauges and thermometers to determine if the mixing chamber temperature is within specified limits, and turn valves to control the temperature. | —0 | —0 |
Clean and sterilize vats and factory processing areas. | —0 | —0 |
Press switches and turn knobs to start, adjust, and regulate equipment, such as beaters, extruders, discharge pipes, and salt pumps. | —0 | —0 |
Observe and listen to equipment to detect possible malfunctions, such as leaks or plugging, and report malfunctions or undesirable tastes to supervisors. | —0 | —0 |
Set up, operate, and tend equipment that cooks, mixes, blends, or processes ingredients in the manufacturing of food products, according to formulas or recipes. | —0 | —0 |
Mix or blend ingredients, according to recipes, using a paddle or an agitator, or by controlling vats that heat and mix ingredients. | —0 | —0 |
Follow recipes to produce food products of specified flavor, texture, clarity, bouquet, or color. | —0 | —0 |
Select and measure or weigh ingredients, using English or metric measures and balance scales. | —0 | —0 |
Turn valve controls to start equipment and to adjust operation to maintain product quality. | —0 | —0 |
Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients. | —0 | —0 |
Give directions to other workers who are assisting in the batchmaking process. | —0 | —0 |
Examine, feel, and taste product samples during production to evaluate quality, color, texture, flavor, and bouquet, and document the results. | —0 | —0 |
Modify cooking and forming operations based on the results of sampling processes, adjusting time cycles and ingredients to achieve desired qualities, such as firmness or texture. | —0 | —0 |
Fill processing or cooking containers, such as kettles, rotating cookers, pressure cookers, or vats, with ingredients, by opening valves, by starting pumps or injectors, or by hand. | —0 | —0 |
Homogenize or pasteurize material to prevent separation or to obtain prescribed butterfat content, using a homogenizing device. | —0 | —0 |
Inspect vats after cleaning to ensure that fermentable residue has been removed. | —0 | —0 |
Test food product samples for moisture content, acidity level, specific gravity, or butter-fat content, and continue processing until desired levels are reached. | —0 | —0 |
Inspect and pack the final product. | —0 | —0 |
Cool food product batches on slabs or in water-cooled kettles. | —0 | —0 |
Operate refining machines to reduce the particle size of cooked batches. | —0 | —0 |
Place products on carts or conveyors to transfer them to the next stage of processing. | —0 | —0 |
Manipulate products, by hand or using machines, to separate, spread, knead, spin, cast, cut, pull, or roll products. | —0 | —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 for each food product batch, such as the ingredients used, temperature, test results, and time cycle.O*NET Task ID 4919 | 1.0 |
Determine mixing sequences, based on knowledge of temperature effects and of the solubility of specific ingredients.O*NET Task ID 4929 | 1.0 |
Formulate or modify recipes for specific kinds of food products.O*NET Task ID 4936 | 0.5 |
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