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Detector tell · 4 patterns · weight 2

The connectors that convict formal human writing

Four rows, weight 2 each, matching the standard connectives of academic and administrative English. They are the only phrase family with a verdict floor of its own, shared with the structural markers.

Our detector calls this category “Formal connector”. The patterns below are read out of functions/api/tools/detect.js when this page is built. A match is a fact about a phrase, not about who wrote the text.

Where this phrase showed up

Appeared in 2 of the human samples and 3 of the AI samples, 5 occurrences in total.

Human-written academic writing, authors at non-anglophone institutions

Matched: “Consequently”

  • “Consequently, the students solved the problem correctly.”

Source:
Katarina Tri Utaminingtyas, Rachmadina Eka Herdianti, Inti Hayatul Fitria & Anton Prayitno, "Small Groups: Student Productive Interactions in Learning Cooperative (Case Study of Mathematics Learning at Junior High School in Pakis, Malang)", Educational Process: International Journal 6(2), 2017
Licence:
CC BY (journal-level license recorded in DOAJ for Educational Process: International Journal, ISSN 2147-0901)
How we know the date:
Published 15 June 2017 — verified against Crossref (api.crossref.org/works/10.22521/edupij.2017.62.3 returns published date-parts [2017,6,15]) — over five years before any general-purpose LLM was publicly available, so human authorship is a date fact. Non-native authorship is a strong inference rather than a certainty: all four author names are Indonesian, and the study was run in a junior high school in Pakis, Malang, Indonesia (stated in the article title). The prose carries textbook L1-Indonesian transfer features (subject-verb disagreement, missing articles) that no copy-editor removed.
Human-written government notice

Matched: “Additionally”

  • “Additionally, this rulemaking broadens the opportunities for military instructor pilots or pilot examiners to obtain civilian ratings based on military experience, expands opportunities for logging pilot time, and removes a burden from sport pilot instructors by permitting them to serve as safety pilots.”

Source:
Federal Aviation Administration, "Regulatory Relief: Aviation Training Devices; Pilot Certification, Training, and Pilot Schools; and Other Provisions", final rule, Federal Register document 2018-12800
Licence:
Public domain — work of the US federal government (17 U.S.C. §105)
How we know the date:
Publication date 2018-06-27, returned by the Federal Register API for document_number 2018-12800; the document number itself encodes the year and is a permanent citable identifier. Four years before public LLMs, and produced by a federal agency under notice-and-comment rulemaking.
AI-written AI asked plainly

Matched: “Furthermore”

  • “Furthermore, advances in collaboration technology have largely eliminated the practical barriers that once made remote arrangements difficult to sustain.”

How we know the date:
No human wrote or edited any part of this text — it was produced token-by-token by the model in the session that wrote this file, and the session transcript is the primary record. The prompt was a bare content request with no style instruction, so this is the model's untouched default register. Residual risk (stated, not dismissed): the model could in principle have reproduced memorized training text; that was not independently checked against a plagiarism index.
AI-written AI asked plainly

Matched: “Additionally”

  • “Additionally, language learning provides direct access to another culture, allowing learners to engage with literature, film, and everyday conversation in their original form rather than through translation.”

How we know the date:
No human wrote or edited any part of this text — it was produced token-by-token by the model in the session that wrote this file, and the session transcript is the primary record. Bare content request, no style instruction; the em-dash is the model's own default punctuation habit, not an instructed tell. Residual risk (stated, not dismissed): the model could in principle have reproduced memorized training text; that was not independently checked against a plagiarism index.
AI-written AI asked plainly

Matched: “Moreover”

  • “Moreover, the reputational damage often outlasts the technical incident itself, as customers hesitate to share payment information with a company that has already lost it once.”

How we know the date:
No human wrote or edited any part of this text — it was produced token-by-token by the model in the session that wrote this file, and the session transcript is the primary record. Bare summarization request with no style instruction. Residual risk (stated, not dismissed): the model could in principle have reproduced memorized training text; that was not independently checked against a plagiarism index.

2 of those samples were written by a person, years before any general-purpose model existed, with a licence and a publication date on record. 3 were machine-written. That is the whole reason these pages exist: a hit here is a fact about a phrase, not about an author.

A constructed example — written by us, not evidence

This sentence was written for this check. It is not from the licensed samples, nobody wrote it as real prose, and it says nothing about how anyone writes. Its only job is to prove the rule still fires, and the generator fails the build if it stops.

“Moreover, the survey was repeated in March. Furthermore, response rates rose.”

  • /\bmoreover\b/gi → “Moreover”
  • /\bfurthermore\b/gi → “Furthermore”

What it means when a person writes this way

These four words are what people were taught to write. They are the connective tissue of a legal notice, a grant application, a lab report and a translated abstract, and a writer using them is usually doing exactly what their register asks of them. The human samples above that trigger this label are documents with a licence and a publication date on record years before any general-purpose model existed. There is no question about who wrote them.

The patterns, as the engine holds them

4 of the 70 rows in our phrase table carry this label. They are printed here as they are written, because a paraphrase of a regular expression is a different regular expression.

/\bmoreover\b/gi weight 2

Matches “moreover” , in any capitalisation .

/\bfurthermore\b/gi weight 2

Matches “furthermore” , in any capitalisation .

/\badditionally\b/gi weight 2

Matches “additionally” , in any capitalisation .

/\bconsequently\b/gi weight 2

Matches “consequently” , in any capitalisation .

What a hit does to your score

The phrase model is one of four in our ensemble, and its formula is patternScore = min(98, max(5, 10 + 7 x total weight)). One occurrence of the heaviest row in this category takes that model from 10 to 24 out of 100 — and the pattern model carries 40% of the ensemble when the em-dash signal fires and 55% when it does not.

This is the only phrase family in the engine with a verdict floor of its own. Hits of this label and of "Structural marker" are counted together: three of them force the score to at least 68, four to at least 78, regardless of what every other model in the ensemble says. If you write formal English and a detector has convicted you, this is the most likely mechanism.

Check your own text

Our detector is free and shows you which sentences it flagged and why, including this category. Treat the score as a starting point, not proof.

Run the detector If you have been accused Why formal writing gets flagged

Other things the detector looks for