「pbl」の検索結果

研学营地教育 (研学旅行与营地教育（体验式教育新赛道，从业者 + 运营视角）— 中国 2016 年教育部《关于推进中小学生研学旅行的意见》政策驱动后形成的细分赛道，含 (a) 子赛道：自然教育 / 科技研学 / 红色研学（爱国主义+党史）/ 历史人文研学 / 国际游学营地 / 户外营地（夏令营·冬令营）；(b) 课程设计：项目制(PBL)·探究式·体验式学习设计、研学手册、学习目标与学科融合、课程评价；(c) 营地与基地运营：安全管理（师生比·应急预案·保险·食宿交通）、营期排期、辅导员(camp counselor)/研学导师排班、基地/营地建设与等级认定；(d) 政策合规：教育部研学旅行政策、研学实践教育基地/营地认定、营地与户外教育团体标准、双减后校外培训监管边界；(e) 商业模式：B 端（学校集采·教育局采购·公立校综合实践课时）、C 端（家庭付费夏令营·亲子）、渠道分销（旅行社·机构代理）、获客与履约。核心矛盾：教育属性 vs 旅游属性、安全 vs 体验、标准化课程 vs 在地化资源。不含：纯 K12 学科培训 / 留学中介 / 纯旅行社跟团 / 学校内 STEAM 硬件采购。) Master OS — automated mastery of 研学旅行与营地教育（体验式教育新赛道，从业者 + 运营视角）— 中国 2016 年教育部《关于推进中小学生研学旅行的意见》政策驱动后形成的细分赛道，含 (a) 子赛道：自然教育 / 科技研学 / 红色研学（爱国主义+党史）/ 历史人文研学 / 国际游学营地 / 户外营地（夏令营·冬令营）；(b) 课程设计：项目制(PBL)·探究式·体验式学习设计、研学手册、学习目标与学科融合、课程评价；(c) 营地与基地运营：安全管理（师生比·应急预案·保险·食宿交通）、营期排期、辅导员(camp counselor)/研学导师排班、基地/营地建设与等级认定；(d) 政策合规：教育部研学旅行政策、研学实践教育基地/营地认定、营地与户外教育团体标准、双减后校外培训监管边界；(e) 商业模式：B 端（学校集采·教育局采购·公立校综合实践课时）、C 端（家庭付费夏令营·亲子）、渠道分销（旅行社·机构代理）、获客与履约。核心矛盾：教育属性 vs 旅游属性、安全 vs 体验、标准化课程 vs 在地化资源。不含：纯 K12 学科培训 / 留学中介 / 纯旅行社跟团 / 学校内 STEAM 硬件采购。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 研学旅行与营地教育（体验式教育新赛道，从业者 + 运营视角）— 中国 2016 年教育部《关于推进中小学生研学旅行的意见》政策驱动后形成的细分赛道，含 (a) 子赛道：自然教育 / 科技研学 / 红色研学（爱国主义+党史）/ 历史人文研学 / 国际游学营地 / 户外营地（夏令营·冬令营）；(b) 课程设计：项目制(PBL)·探究式·体验式学习设计、研学手册、学习目标与学科融合、课程评价；(c) 营地与基地运营：安全管理（师生比·应急预案·保险·食宿交通）、营期排期、辅导员(camp counselor)/研学导师排班、基地/营地建设与等级认定；(d) 政策合规：教育部研学旅行政策、研学实践教育基地/营地认定、营地与户外教育团体标准、双减后校外培训监管边界；(e) 商业模式：B 端（学校集采·教育局采购·公立校综合实践课时）、C 端（家庭付费夏令营·亲子）、渠道分销（旅行社·机构代理）、获客与履约。核心矛盾：教育属性 vs 旅游属性、安全 vs 体验、标准化课程 vs 在地化资源。不含：纯 K12 学科培训 / 留学中介 / 纯旅行社跟团 / 学校内 STEAM 硬件采购。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「研学」「研学旅行」「营地教育」「研学营地」「自然教育」

日本語の概要は準備中です。原文の説明を表示しています。

Present project-design pathway options and orchestrate regenerative projects using backwards design, PBL, SEEDS, compassionate systems action, or civic/service pathways.

日本語の概要は準備中です。原文の説明を表示しています。

Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.

日本語の概要は準備中です。原文の説明を表示しています。

Present project-design pathway options and orchestrate regenerative projects using backwards design, PBL, SEEDS, compassionate systems action, or civic/service pathways.

日本語の概要は準備中です。原文の説明を表示しています。

Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when upgrading or migrating an Android project from any legacy Google Play Billing Library (PBL) version to the latest stable version of PBL.

日本語の概要は準備中です。原文の説明を表示しています。

The map for reading and editing an OpenMAIC stage document with read_stage, patch_stage, and grep_stage. Load it before patching a structure you have not patched before, when patch_stage rejects an operation, or whenever the path from a stage, outline, scene, content object, or action to the field you need is uncertain. It routes to field-level references for quizzes, interactive widgets, actions, and PBL projects; the installed slide-dsl skill remains the complete slide canvas manual.

日本語の概要は準備中です。原文の説明を表示しています。

Guides conversion of a pre-.NET 8 Blazor Server app into a .NET 8+ Blazor Web App. USE FOR: migrating apps that use AddServerSideBlazor and MapBlazorHub to the AddRazorComponents/MapRazorComponents model, converting _Host.cshtml to an App.razor root component, replacing blazor.server.js with blazor.web.js, migrating CascadingAuthenticationState to a service, adopting new Blazor Web App features like enhanced navigation and streaming rendering. DO NOT USE FOR: apps that are already Blazor Web Apps (already use AddRazorComponents and MapRazorComponents), Blazor WebAssembly or hosted Blazor WebAssembly apps (different migration path), apps that should stay on the Blazor Server hosting model without converting, or apps still targeting .NET Framework.

日本語の概要は準備中です。原文の説明を表示しています。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

日本語の概要は準備中です。原文の説明を表示しています。

Map curriculum connections across multiple subjects for a real-world problem or authentic context. Use when planning cross-curricular projects or connecting content to real issues.

日本語の概要は準備中です。原文の説明を表示しています。

Guides conversion of a pre-.NET 8 Blazor Server app into a .NET 8+ Blazor Web App. USE FOR: migrating apps that use AddServerSideBlazor and MapBlazorHub to the AddRazorComponents/MapRazorComponents model, converting _Host.cshtml to an App.razor root component, replacing blazor.server.js with blazor.web.js, migrating CascadingAuthenticationState to a service, adopting new Blazor Web App features like enhanced navigation and streaming rendering. DO NOT USE FOR: apps that are already Blazor Web Apps (already use AddRazorComponents and MapRazorComponents), Blazor WebAssembly or hosted Blazor WebAssembly apps (different migration path), apps that should stay on the Blazor Server hosting model without converting, or apps still targeting .NET Framework.

日本語の概要は準備中です。原文の説明を表示しています。

Map curriculum connections across multiple subjects for a real-world problem or authentic context. Use when planning cross-curricular projects or connecting content to real issues.

日本語の概要は準備中です。原文の説明を表示しています。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

日本語の概要は準備中です。原文の説明を表示しています。

Knowledge base from MIL-HDBK-61B, the DoD Configuration Management (CM) guidance handbook (7 April 2020, cataloged with Change 1, 2025). Use for defense CM across the acquisition and sustainment life cycle: the five CM functions (planning, identification, control/change management, status accounting, verification & audit), the three configuration baselines (FBL/ABL/PBL) and FCD→ACD→PCD documentation, configuration items (CIs/HWCI/CSCI), change instruments (ECP/NOR/RFV), the CCB and Class I/II classification, CDCA/AA authority, FCA/PCA audits, data management and data rights (CDRL/DID, DFARS rights ladder, master/authoritative source), and tailoring CM by phase (EIA-649-1 R/T/NR matrix, Appendix C templates), plus digital-era CM (digital twin, viewpoints, MOSA). NOTE: 61B is advisory guidance that adopts and points to the SAE EIA-649 / EIA-649-1 / GEIA-HB-649 suite for the authoritative CM requirements — this pack names and describes that suite but does not reproduce its copyrighted requirement text. Thin on: the EIA-649 requirement clauses themselves, form-level detail (DD-form field instructions, full DID content), and non-DoD/commercial CM frameworks.

日本語の概要は準備中です。原文の説明を表示しています。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

日本語の概要は準備中です。原文の説明を表示しています。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

日本語の概要は準備中です。原文の説明を表示しています。