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Record W2254077678 · doi:10.1002/cjce.22423

How do you write and present research well? 5 –revise sentences over 30 words long

2015· article· en· W2254077678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRedundancy (engineering)SentenceComputer scienceBoosting (machine learning)Interface (matter)Rest (music)Natural language processingHuman–computer interactionArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Abstract Fewer words can be more powerful than many words. Identify boosting, metadiscourse, redundancy, hedging, feeble sentences, and convoluted expressions in the following sentence. [1] “It should be noted that while we have already mentioned the interaction of the implants with the tissue and will discuss later in this manuscript the effect of the regenerative therapies on the tissues, the possibility exists that the materials delivered to the interface may interact with the implant itself. We will briefly discuss this possibility now before moving forward with the rest of our discussion.” [2] What is the minimum number of words to express the same information? less than 10 : “Materials at the interface can interact with the implant.” from 10 to 19 from 20 to 29 30 or more

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.254
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it