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Record W4317876078 · doi:10.1002/bies.202200167

Interpreting fossilized nervous tissues

2023· article· en· W4317876078 on OpenAlexaff
Cédric Aria, Jean Vannier, Tae‐Yoon S. Park, Robert R. Gaines

Bibliographic record

VenueBioEssays · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
Fundersnot available
KeywordsInterpretation (philosophy)TaphonomyBiologySet (abstract data type)Task (project management)Variety (cybernetics)PaleontologyEvolutionary biologyAnatomyNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Paleoneuranatomy is an emerging subfield of paleontological research with great potential for the study of evolution. However, the interpretation of fossilized nervous tissues is a difficult task and presently lacks a rigorous methodology. We critically review here cases of neural tissue preservation reported in Cambrian arthropods, following a set of fundamental paleontological criteria for their recognition. These criteria are based on a variety of taphonomic parameters and account for morphoanatomical complexity. Application of these criteria shows that firm evidence for fossilized nervous tissues is less abundant and detailed than previously reported, and we synthesize here evidence that has stronger support. We argue that the vascular system, and in particular its lacunae, may be central to the understanding of many of the fossilized peri-intestinal features known across Cambrian arthropods. In conclusion, our results suggest the need for caution in the interpretation of evidence for fossilized neural tissue, which will increase the accuracy of evolutionary scenarios. Also see the video abstract here: https://youtu.be/2_JlQepRTb0.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.030
GPT teacher head0.265
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2023
Admission routes1
Has abstractyes

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