رمز O*NET-SOC
17-2051.02

Design or oversee projects involving provision of potable water, disposal of wastewater and sewage, or prevention of flood-related damage. Prepare environmental documentation for water resources, regulatory program compliance, data management and analysis, and field work. Perform hydraulic modeling and pipeline design.

تُعرض أسماء المهن وأوصاف المهام بالإنجليزية كما نُشرت. أما التسميات، ومنها أنواع المهام، فمترجمة.

التعرّض للذكاء الاصطناعي

  • مصدر البيانات: BLSتاريخ النشر: 2026-08

    مرتفع· نسبي

    منخفضأربع فئات نسبيةمرتفع جدًا

    قيمة على مستوى المجموعة المهنية

    المقياس والأساس والمصدر

    أربع فئات نسبية (منخفض / متوسط / مرتفع / مرتفع جدًا)

    831 مهنة تفصيلية في جدول توقعات التوظيف لدى BLS. وتُسند القيمة على مستوى رمز مصفوفة التوظيف الوطنية (NEM)، فالمهن التي تشترك في الرمز نفسه تأخذ النطاق نفسه

    مجموعة البيانات المصدر (تنزيل ملف XLSX)

  • مصدر البيانات: Anthropicتاريخ النشر: 2026-03

    0.008

    0.000نطاق القيم المعروضة هنا0.745

    قيمة على مستوى المجموعة المهنية

    المقياس والأساس والمصدر

    مؤشر التعرّض المرصود، 0–1 كما نُشر

    مربوط بمهام O*NET

    منشورة لكل مهنة في SOC 2018؛ وكل مهنة في O*NET تحمل رمز SOC 2018 نفسه تأخذ هذه القيمة

    مجموعة البيانات المصدر (تنزيل ملف CSV)

  • مصدر البيانات: ILOتاريخ النشر: 2025

    0.30

    0.09نطاق القيم المعروضة هنا0.70

    قيمة على مستوى المجموعة المهنية

    المقياس والأساس والمصدر

    مؤشر التعرّض للذكاء الاصطناعي التوليدي، 0–1 كما نُشر

    قيمة منشورة على مستوى مجموعة الوحدة في ISCO-08. رُبطت بهذه المهنة بتطبيق جداول التناظر الرسمية لمكتب إحصاءات العمل الأمريكي (BLS) (من ISCO-08 إلى SOC 2010، ومن SOC 2010 إلى SOC 2018) كما نُشرت، والتناظر كلي أو جزئي

    مجموعة البيانات المصدر (تنزيل ملف PDF)

ما نوع القيمة التي ينشرها هذا المصدر

فئة BLS رتبة نسبية لا مستوى مطلق، وليست قياسًا مباشرًا: فهي تجمع الرتب المئينية للمهنة في عدة دراسات منشورة في أربع فئات. وهي ليست توقّعًا للتوظيف أو الأجور، ولا احتمالًا للتبني، ولا تفرّق بين الأتمتة والتعزيز.

التعرض للذكاء الاصطناعي (معيار OpenAI)

28 مهمة مقيَّمة · 26 مهمة بقيمة β ≥ 0.5 (92.9%)

β = التعرّض المباشر (E1) + 0.5 × التعرّض عند توفر الأدوات (E2)، وفق تعريف المستودع المصدري.

  • وحدة المصدر: مهام O*NET 27.2 ← رمز المهنة في O*NET 31.0
  • جميع المهام المقيَّمة موجودة في قائمة مهام O*NET 31.0.
المصدر
OpenAI "GPTs are GPTs" exposure rubric
الإصدار
gh-main-0471612
الترخيص
MIT License, Copyright (c) 2024 OpenAI

المهام

أوصاف المهام من O*NET® 31.0 Database، والمهام الأساسية أولًا.

المهمةالنوعβ (OpenAI)
Write technical reports or publications related to water resources development or water use efficiency.أساسية1
Review and critique proposals, plans, or designs related to water or wastewater treatment systems.أساسية0.5
Provide technical support on water resource or treatment issues to government agencies.أساسية0.5
Provide technical direction or supervision to junior engineers, engineering or computer-aided design (CAD) technicians, or other technical personnel.أساسية1
Identify design alternatives for the development of new water resources.أساسية0.5
Develop plans for new water resources or water efficiency programs.أساسية0.5
Design or select equipment for use in wastewater processing to ensure compliance with government standards.أساسية0.5
Conduct water quality studies to identify and characterize water pollutant sources.أساسية0.5
Perform hydrological analyses, using three-dimensional simulation software, to model the movement of water or forecast the dispersion of chemical pollutants in the water supply.أساسية0.5
Perform hydraulic analyses of water supply systems or water distribution networks to model flow characteristics, test for pressure losses, or to identify opportunities to mitigate risks and improve operational efficiency.أساسية0.5
Oversee the construction of decentralized or on-site wastewater treatment systems, including reclaimed water facilities.أساسية0
Gather and analyze water use data to forecast water demand.أساسية0.5
Conduct feasibility studies for the construction of facilities, such as water supply systems, runoff collection networks, water and wastewater treatment plants, or wastewater collection systems.أساسية0.5
Conduct environmental impact studies related to water and wastewater collection, treatment, or distribution.أساسية0.5
Conduct cost-benefit analyses for the construction of water supply systems, runoff collection networks, water and wastewater treatment plants, or wastewater collection systems.أساسية0.5
Analyze the efficiency of water delivery structures, such as dams, tainter gates, canals, pipes, penstocks, or cofferdams.أساسية0.5
Analyze storm water or floodplain drainage systems to control erosion, stabilize river banks, repair channel streams, or design bridges.أساسية0.5
Analyze and recommend sludge treatment or disposal methods.أساسية0.5
Analyze and recommend chemical, biological, or other wastewater treatment methods to prepare water for industrial or domestic use.أساسية0.5
Design water storage tanks or other water storage facilities.أساسية0.5
Design water runoff collection networks, water supply channels, or water supply system networks.أساسية0.5
Design water or wastewater lift stations, including water wells.أساسية0.5
Design water distribution systems for potable or non-potable water.أساسية0.5
Design sludge treatment plants.أساسية0.5
Design pumping systems, pumping stations, pipelines, force mains, or sewers for the collection of wastewater.أساسية0.5
Design domestic or industrial water or wastewater treatment plants, including advanced facilities with sequencing batch reactors (SBR), membranes, lift stations, headworks, surge overflow basins, ultraviolet disinfection systems, aerobic digesters, sludge lagoons, or control buildings.أساسية0
Evaluate the operation and maintenance of water or wastewater systems to identify ways to improve their efficiency.أساسية0.5
Perform mathematical modeling of underground or surface water resources, such as floodplains, ocean coastlines, streams, rivers, or wetlands.تكميلية0.5

معلومات مهنية

المصادر والإسناد

This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.

Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.

O*NET OnLine: 17-2051.02 Water/Wastewater Engineers

KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.

الإسناد والتراخيص كاملة