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Record W1490250179 · doi:10.1111/eve.12252

Extraction techniques for equine incisor and canine teeth

2014· article· en· W1490250179 on OpenAlex
Jennifer E. Rawlinson, James L. Carmalt

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.

Bibliographic record

VenueEquine Veterinary Education · 2014
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineIncisorDentistryCanine toothOrthodonticsLateral incisorMandibular incisorMandibular canineMaxillary central incisor

Abstract

fetched live from OpenAlex

Summary Extraction of equine incisor and canine teeth is a vital part of equine dentistry. Although dental pathology involving the incisor and canine teeth is less common, the practitioner should be prepared to diagnose conditions and develop a treatment plan. Depending on the pathology revealed via oral examination and intraoral radiographs, the treatment plan may include either simple (nonsurgical) or surgical extraction of an incisor or canine tooth. Technique and instrumentation refinement over the last 20 years has led to more precise extraction procedures with reduced secondary trauma to healthy tissues. As a result, incisor and canine extraction procedures are more predictable in execution and quality, with minimal complications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.099
GPT teacher head0.457
Teacher spread0.358 · 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