# Weighers, Measurers, Checkers, and Samplers, Recordkeeping

> Weigh, measure, and check materials, supplies, and equipment for the purpose of keeping relevant records. Duties are primarily clerical by nature. Includes workers who collect and keep record of samples of products or materials.

- **SOC code:** 43-5111.00
- **Canonical URL:** https://singulariki.com/roles/role-43-5111-00
- **Also known as:** Fluid Operator, Inventory Specialist, Quality Assurance Inspector (QA Inspector), Temperature Taker, Cycle Counter, Scale Operator, Supply Clerk, Aircraft Shipping Checker
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Compare product labels, tags, or tickets, shipping manifests, purchase orders, and bills of lading to verify accuracy of shipment contents, quality specifications, or weights.
- Document quantity, quality, type, weight, test result data, and value of materials or products to maintain shipping, receiving, and production records and files.
- Weigh or measure materials, equipment, or products to maintain relevant records, using volume meters, scales, rules, or calipers.
- Collect or prepare measurement, weight, or identification labels and attach them to products.
- Remove from stock products or loads not meeting quality standards, and notify supervisors or appropriate departments of discrepancies or shortages.
- Inspect products and examination records to determine the number of defects per worker and the reasons for examiners' rejections.
- Examine products or materials, parts, subassemblies, and packaging for damage, defects, or shortages, using specification sheets, gauges, and standards charts.
- Store samples of finished products in labeled cartons and record their location.
- Signal or instruct other workers to weigh, move, or check products.
- Count or estimate quantities of materials, parts, or products received or shipped.
- Communicate with customers and vendors to exchange information regarding products, materials, and services.
- Fill orders for products and samples, following order tickets, and forward or mail items.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Production and Processing _(knowledge)_
- Near Vision _(ability)_
- Mathematics _(knowledge)_
- Critical Thinking _(essential_skill)_
- Written Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Category Flexibility _(ability)_
- Reading Comprehension _(essential_skill)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Information Ordering _(ability)_
- Perceptual Speed _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- English Language _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_

**Tools & technology:**
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Oracle Database _(hot technology)_
- SAP software _(hot technology)_
- Inventory management systems _(in demand)_
- Email software
- IBM Notes
- Infor ERP Baan

## AI exposure & outlook

- **AI task-overlap index:** 33rd percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 43rd percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 37th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 26th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 89th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -4.8% growth (Declining); 5.3k annual openings; 49.8k → 47.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $45,650; 49,720 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 67% automation, 19% augmentation (usage-weighted).
- **Autonomy median:** 2.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Prepare measurement tables and conversion charts, using standard formulas. _(1.3% of measured AI use; directive)_
- Weigh or measure materials, equipment, or products to maintain relevant records, using volume meters, scales, rules, or calipers. _(0.3% of measured AI use; directive)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me prepare measurement tables and conversion charts, using standard formulas.
- Help me weigh or measure materials, equipment, or products to maintain relevant records, using volume meters, scales, rules, or calipers.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-43-5111-00_