MétaCan
Menu
Back to cohort

Estimating body mass from silhouettes: testing the assumption of elliptical body cross-sections

2001· article· en· W2111711410 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.

Bibliographic record

VenuePaleobiology · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsRoyal Ontario Museum
Fundersnot available
KeywordsVertebrateComputationTunaSurface (topology)Body shapeBody weightBody surfaceGeologyPaleontologyComputer scienceBiologyMathematicsGeometryAlgorithmFish <Actinopterygii>FisheryArtificial intelligence

Abstract

fetched live from OpenAlex

In computational studies of the body mass and surface area of vertebrates, it is customary to assume that body cross-sections are approximately elliptical. However, a review of actual vertebrate cross-sections establishes that this assumption is not usually met. A new cross-sectional model using superellipses is therefore introduced, together with a scheme that allows estimates to be given with ranges. Tests of the new method, using geometrical shapes, miniature vertebrate models, and actual animals, show that the method has a high accuracy in body mass estimation. A new computer program to perform the computation is introduced. The application of the method to some Mesozoic marine reptiles suggests that the tuna-shaped ichthyosaur Stenopterygius probably had body masses comparable to those of average cetaceans of the same body length.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.268
Teacher spread0.233 · 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