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Your custom message can be found under. A rejection e-mail looks like this: All demands will be sent out to at least one publisher email address for approval.
This typically indicates that sales reps who already work with particular accounts have the chance to authorize demands from them. The other recipients of the request emails must show up to you in the 'To:' line of the email. This likewise means that it is possible that another person will have managed a demand before you get to it.
As you write, Car Linker quietly highlights the words that match your other notes and offers a oneclick wikilink ranked by a confidence rating (not a blind text match), so you get to select the useful links and not the sound. This software application was vibe-coded. Car Linker always suggests and never links by itself.
import ready GSA SER projectsA lot of "automobile link" tools do a match: a word either equals a note title or it does not - Auto Linker rather calculates a for every candidate and only shows the ones that clear a limit you control. That rating blends a number of signals: how carefully the text matches a note's title (exact, stemmed, or a close typo).
No difficult stopword list; it's graded, and selftunes to your vault via IDF. AND reads differently from and; ALLCAPS/ TitleCase get an increase, lowercase function words a penalty. welllinked "center" notes rank higher (PageRank over your link graph). (optional) an ondevice embedding design reranks candidates by meaning. it gains from the links you really make and the recommendations you accept.
Premium vs Raw Link Lists for 2026 SEO
Steps 58 just push the score up or down a note is removed only at the steps marked. the text is divided into tokens (keeping intraword bits like the dot in 802.1 q), noting where expression vs anchor separators fall. spans inside [[ links]], #tags, inline 'code' and "'fenced blocks are omitted up front.
a spannote you've rejected (pernote or vaultwide), or the note you're currently editing, is dropped before scoring. how textually total the match is: entire title = 1.0; a subphrase or single word = the share of the title's details (IDF) it covers; a digitbase 0.85; a typo (1 edits length).
a lone AND/ Database gets a boost and a lowercase common word a penalty; neutral for multiword periods. includes PageRank (center notes), semantic resemblance (if the design is on) and your accept history (if any), then renormalizes everything to a single 01 confidence. candidates that do not clear the bar are dropped.
survivors are highlighted with an approve/reject tooltip: a highlight suggests text decided it, a one suggests meaning was the choosing lift. In short, a note leaves when it shares no word (3 ), sits in a skipped/rejected/self area (2, 4), scores too low (9 ), or loses to an overlapping much better match (10 ).
import ready GSA SER projectsHow to Scale SEO Growth with Verified Lists
tune the balance yourself; bring back defaults anytime. handles 802.1 Q, Topic: Subtopic, client-server, and pathlike titles by means of configurable separator guidelines. a contiguous run of a title's words matches the whole note (typing approach 3 - CAD suggests Payment technique 3 - CAD), scored by just how much of the title it covers.
, consisting of an unique word standing in for the entire (kruger DunningKruger). link [[ Dinosaurraptor]] as soon as and raptor can suggest Dinosaur thereafter, provided a confident rating. dismiss a tip pernote or vaultwide; manage and restore them in settings. the target note, authorize (), or reject () inline. Ctrl/Cmd + inserts a bare [[ Note]] (no display screen alias); Shift + authorizes every repeat of that tip in the note at when.
pushing you toward atomic, outwarddefined notes. Everything runs. The index, scores, turn down list, discovered aliases, and the optional embedding cache all remain on your device no telemetry, no external API. The network occasion in the entire plugin is the optional, one-time download of the embedding model when you turn the semantic tier on (it brings no note information, and can be avoided totally by pointing at a design you already have).
When you allow it you choose: the default multilingual model (Xenova/paraphrase-multilingual-MiniLM-L12-v2, 50 MB, when), or point at a format design currently on disk (airgapped/ your own). Use to precompute every note's "implying finger print" so meaningbased ranking is all set across the entire vault right away. The semantic tier actual candidates it fine-tunes what the text match currently discovered.
