Log-Shape Ratios, Procrustes Superimposition, Elliptic Fourier Analysis: Three Worked Examples in R
Bibliographic record
Abstract
This publication uses and presents R routines that perform various morphometric analysis in the context of rodent systematics. The morphological variation of two commensal rat species, Rattus exulans and Rattus tanezumi , is analysed and the potential for discrimination between the two is assessed. Specimens were trapped in three localities of Northern and North-Eastern Thailand. Shape and size variation are analysed in regards to sex, species, and geographical effects with various morphometric methods: log-shape ratios on body measurements, elliptic Fourier analyses on teeth outlines, Procrustes superimposition on skull coordinates. Both species are significantly different; however, the discrimination seems to be better on skull Procrustes coordinates and on teeth size than on other morphometric data set. Where different allometries exist between species and where species differ in size and shape, it is shown that filtering allometry using the approach of Burnaby (1966) can improve the discrimination between species. Sex size and shape dimorphism is reduced by comparison to interindividual variation. Shape variation varies between sampled localities for Rattus exulans , this is not the case for Rattus tanezumi . This pattern is possibly related to the more commensal life of R. exulans . Download the complete Yellow Book on Virtual Morphology and Evolutionary Morphometrics in the new millenium.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.055 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".