Choosing Verified Data vs Raw Targets
Your customized message can be discovered under. A rejection email looks like this: All requests will be sent out to at least one publisher e-mail address for approval. Your organization can identify a single default e-mail address for all DRCs, or one default per sales division, imprint or group of imprints, and even an individual (such as a press agent) related to a title or group of titles.
This usually indicates that sales reps who currently deal with specific accounts have the opportunity to authorize requests from them. The other receivers of the request emails should show up to you in the 'To:' line of the e-mail. This also means that it is possible that another person will have managed a demand before you get to it.
As you write, Auto Linker silently highlights the words that match your other notes and uses a oneclick wikilink ranked by a confidence rating (not a blind text match), so you get to choose the beneficial links and not the noise. This software application was vibe-coded. Car Linker always suggests and never links by itself.
GSA SER guide setup VPS and listsMost "vehicle link" tools do a match: a word either equals a note title or it doesn't - Auto Linker rather calculates a for each candidate and only shows the ones that clear a threshold you control. That rating mixes several signals: how carefully the text matches a note's title (specific, stemmed, or a close typo).
No tough stopword list; it's graded, and selftunes to your vault through IDF. AND checks out in a different way from and; ALLCAPS/ TitleCase get an increase, lowercase function words a penalty. welllinked "center" notes rank greater (PageRank over your link chart). (optional) an ondevice embedding design reranks prospects by meaning. it discovers from the links you actually make and the tips you accept.
Choosing Verified Data vs Scraped Targets
Actions 58 just push the score up or down a note is gotten rid of just at the actions marked., #tags, inline 'code' and "'fenced blocks are excluded up front.
a spannote you have actually declined (pernote or vaultwide), or the note you're presently modifying, is dropped before scoring. how textually complete the match is: whole title = 1.0; a subphrase or single word = the share of the title's info (IDF) it covers; a digitbase 0.85; a typo (1 edits length).
an only AND/ Database gets a boost and a lowercase common word a penalty; neutral for multiword spans. adds PageRank (center notes), semantic resemblance (if the model is on) and your accept history (if any), then renormalizes everything to a single 01 confidence. candidates that don't clear the bar are dropped.
survivors are underlined with an approve/reject tooltip: an underline indicates text chose it, a one suggests meaning was the choosing lift. Simply put, a note leaves when it shares no word (3 ), sits in a skipped/rejected/self area (2, 4), ratings too low (9 ), or loses to an overlapping much better match (10 ).
GSA SER guide setup VPS and listsEssential Tips for Managing Automated Backlink Lists
tune the balance yourself; bring back defaults anytime. deals with 802.1 Q, Subject: Subtopic, client-server, and pathlike titles through configurable separator guidelines. an adjoining run of a title's words matches the entire note (typing approach 3 - CAD suggests Payment approach 3 - CAD), scored by how much of the title it covers.
, consisting of a distinctive word standing in for the entire (kruger DunningKruger). link [[ Dinosaurraptor]] once and raptor can suggest Dinosaur afterwards, provided a positive score. dismiss a suggestion pernote or vaultwide; manage and restore them in settings. the target note, approve (), or turn down () inline. Ctrl/Cmd + inserts a bare [[ Keep in mind]] (no display screen alias); Shift + approves every repeat of that tip in the note at when.
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 whole plugin is the optional, onetime download of the embedding model when you turn the semantic tier on (it carries no note data, and can be prevented completely by pointing at a model you currently have).
When you allow it you pick: the default multilingual design (Xenova/paraphrase-multilingual-MiniLM-L12-v2, 50 MB, as soon as), or point at a format design currently on disk (airgapped/ your own). Usage to precompute every note's "implying finger print" so meaningbased ranking is all set throughout the entire vault instantly. The semantic tier literal prospects it refines what the text match already discovered.
