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Record W2135994457 · doi:10.1139/z02-008

Modeling the distance between the molar tooth rows in mammals

2002· article· en· W2135994457 on OpenAlexvenueno aff
Walter Stalker Greaves

Bibliographic record

VenueCanadian Journal of Zoology · 2002
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIncisorBite force quotientMolarPerpendicularSkullRowAnatomyOrthodonticsBiologyGeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

The sum of all possible bite forces along a mammalian tooth row is related to the area under the curve when bite force is plotted from one end of the tooth row to the other. Integrating the equation of this plot and dividing by the length of the entire jaw, from joint to incisor, gives the average bite force along the entire jaw (as opposed to along the tooth row). Calculations indicate that for any jaw shape there is only one location for the tooth row relative to the midline of the skull, where the average bite force is maximized; the average force is lower when the tooth row is closer to, or farther from, the midline. In addition, for animals with long narrow jaws, the location where this maximum is realized is relatively closer to the midline than it is for animals with short wide jaws. In many mammals, the distance between the jaw joints (jaw width) often varies between 60 and 80% of the distance from the jaw joints to the incisor (jaw length) in narrow and wide jaws, respectively. Length is measured perpendicular to the resultant force of the jaw muscles. Accepting that average bite force will be maximized, the model predicts that in the longer, narrower jaws the distance between the two molar rows will be approximately half the width of the jaw (and will approach 60% in the shorter, wider jaws).

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.285
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2002
Admission routes1
Has abstractyes

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