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Record W2522975138 · doi:10.1098/rsbl.2016.0528

The fragmentation of Pangaea and Mesozoic terrestrial vertebrate biodiversity

2016· article· en· W2522975138 on OpenAlexaff
Matthew J. Vavrek

Bibliographic record

VenueBiology Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsRoyal Ontario Museum
Fundersnot available
KeywordsBiologyVertebratePangaeaMesozoicBiodiversityFragmentation (computing)PaleontologyEcologyEvolutionary biologyGenetics

Abstract

fetched live from OpenAlex

During the Mesozoic (242-66 million years ago), terrestrial regions underwent a massive shift in their size, position and connectivity. At the beginning of the era, the land masses were joined into a single supercontinent called Pangaea. However, by the end of the Mesozoic, terrestrial regions had become highly fragmented, both owing to the drifting apart of the continental plates and the extremely high sea levels that flooded and divided many regions. How terrestrial biodiversity was affected by this fragmentation and large-scale flooding of the Earth's landmasses is uncertain. Based on a model using the species-area relationship (SAR), terrestrial vertebrate biodiversity would be expected to nearly double through the Mesozoic owing to continental fragmentation, despite a decrease of 24% in total terrestrial area. Previous studies of Mesozoic vertebrates have generally found increases in terrestrial diversity towards the end of the era, although these increases are often attributed to intrinsic or climatic factors. Instead, continental fragmentation over this time may largely explain any observed increase in terrestrial biodiversity. This study demonstrates the importance that non-intrinsic effects can have on the taxonomic success of a group, and the importance of geography to understanding past biodiversity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.182

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)0.0000.000

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

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