1import{At as e,S as t,V as n,X as r,at as i,ct as a,dt as o,ft as s,g as c,i as l,k as u,kt as d,tt as f,yt as p,z as m}from"./BjMc0cMy.js";import"./0n9TOsVt.js";import{n as h,o as g,t as _}from"./D54Dnuj7.js";var v={"/":{title:``,desc:`Reduce noise in decision making by structuring setup, scoring, and comparison in one workflow.`,changefreq:`weekly`,priority:1},"/blog":{title:`Blog`,desc:`Notes on structured decision making, ranking methods, and practical evaluation workflows.`,changefreq:`weekly`,priority:.7},"/agents":{title:`Use Plought with your AI`,desc:`Let a compatible AI agent help set up, evaluate, and review a decision in Plought.`,changefreq:`monthly`,priority:.7},"/setup":{title:`Start`,desc:`Set your decision title and goal so every criterion and comparison stays aligned.`,changefreq:`weekly`,priority:.9},"/setup/alternatives":{title:`Alternatives`,desc:`Add and refine alternatives before running scoring and comparison methods.`,changefreq:`weekly`,priority:.9},"/setup/criteria":{title:`Criteria`,desc:`Define the criteria used to evaluate alternatives consistently across methods.`,changefreq:`weekly`,priority:.9},...Object.fromEntries(_.map(e=>[e.path,{title:e.title,desc:e.desc,changefreq:`weekly`,priority:.8}])),"/analysis":{title:`Summary`,desc:`Compare included methods side by side and review consensus guidance before deciding.`,changefreq:`weekly`,priority:.9},"/analysis/weight":{title:`Weighted Sum`,desc:`Review weighted totals and criterion contribution breakdown for each alternative.`,changefreq:`weekly`,priority:.8},"/analysis/compare":{title:`Pairwise Comparison`,desc:`Inspect pairwise outcomes and normalized totals across all alternatives.`,changefreq:`weekly`,priority:.8},"/analysis/allocate":{title:`Point Allocation`,desc:`Analyze weighted results from per-criterion point allocations.`,changefreq:`weekly`,priority:.8},"/analysis/topsis":{title:`TOPSIS`,desc:`Evaluate alternatives by distance to ideal and anti-ideal profiles.`,changefreq:`weekly`,priority:.8},"/analysis/robustness":{title:`Robustness`,desc:`Test recommendation stability under simulated weight variation.`,changefreq:`weekly`,priority:.8}},y=e=>e.length>1&&e.endsWith(`/`)?e.slice(0,-1):e,b=e=>v[y(e)]??null;Object.keys(v).sort();var x=n(`<link rel="canonical"/> <meta name="description"/> <meta property="og:type" content="website"/> <meta property="og:site_name"/> <meta property="og:title"/> <meta property="og:description"/> <meta property="og:url"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title"/> <meta name="twitter:description"/> <!>`,1);function S(n,_){e(_,!0);let v=l(_,`title`,3,``),y=l(_,`desc`,3,``),S=p(()=>b(g.url.pathname)),C=h.url.replace(/\/$/,``),w=p(()=>g.url.pathname.length>1&&g.url.pathname.endsWith(`/`)?g.url.pathname.slice(0,-1):g.url.pathname),T=p(()=>new URL(r(w)||`/`,`${C}/`).toString()),E=p(()=>v()||r(S)?.title||``),D=p(()=>r(E)?`${r(E)} | ${h.name}`:h.name),O=p(()=>y()||r(S)?.desc||h.tagline),k=p(()=>JSON.stringify({"@context":`https://schema.org`,"@graph":[{"@type":`WebSite`,name:h.name,url:C,description:h.tagline},{"@type":`WebApplication`,name:h.name,applicationCategory:`BusinessApplication`,operatingSystem:`Web`,url:r(T),description:r(O)}]}).replaceAll(`<`,`\\u003c`)),A=p(()=>`<script type="application/ld+json">${r(k)}<\/script>`);t(`1uve6uq`,e=>{var t=x(),n=o(t),l=s(n,2),d=s(l,4),p=s(d,2),g=s(p,2),_=s(g,2),v=s(_,4),y=s(v,2),b=s(y,2);u(b,()=>r(A)),i(()=>{c(n,`href`,r(T)),c(l,`content`,r(O)),c(d,`content`,h.name),c(p,`content`,r(D)),c(g,`content`,r(O)),c(_,`content`,r(T)),c(v,`content`,r(D)),c(y,`content`,r(O))}),f(()=>{a.title=r(D)??``}),m(e,t)}),d()}export{S as t};
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.