MétaCan
Menu
Back to cohort

The importance of scientific collecting and natural history museums for comparative neuroanatomy

2011· review· en· W2097027601 on OpenAlexaff
Andrew N. Iwaniuk

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2011
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsVariety (cybernetics)NeuroanatomyNatural historyMarsupialVertebrateNatural (archaeology)Resource (disambiguation)Data scienceComputer scienceBiologyNeuroscienceEcologyPaleontologyArtificial intelligence

Abstract

fetched live from OpenAlex

The comparative study of vertebrate brains is inherently dependent upon access to a sufficient number of species and specimens to perform meaningful comparisons. Although many studies rely on compiling published information, continued specimen collection, in addition to more extensive use of existing brain collections and natural history museums, are crucial for detailed neuroanatomical comparisons across species. This review highlights the importance of collecting species through a variety of means, details a marsupial brain collection, and stresses the potential of natural history museums as a resource for comparative neuroanatomy. By taking advantage of as many of these resources as possible, researchers can rapidly increase species coverage and generate a better understanding of how the brain evolves.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.681
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.284
GPT teacher head0.377
Teacher spread0.093 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the New York Academy of SciencesSame topicEvolution and Paleontology StudiesFrench-language works237,207