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Record W2325184616 · doi:10.1144/m38.26

Chapter 26 Graptolite palaeobiogeography

2013· article· en· W2325184616 on OpenAlexaff
Daniel Goldman, Jörg Maletz, Michael J. Melchin, Junxuan Fan

Bibliographic record

VenueGeological Society London Memoirs · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsBiologyComputer sciencePaleontologyGeology

Abstract

fetched live from OpenAlex

Abstract Graptolite faunas exhibited strong biogeographical differentiation during the Early Palaeozoic, particularly in the Ordovician. Skevington recognized two major faunal provinces, the high to mid palaeolatitude ‘Atlantic Province’ and the low-palaeolatitude ‘Pacific Province’. Subsequent workers have generally accepted this pattern of graptolite distribution, but the controls on this pattern have been the subject of considerable debate. Two competing models have emerged: a surface water temperature model and a depth stratification model. It is likely that the some of the physical and chemical oceanic factors that vary with latitude may also vary in a similar way along an onshore to offshore transect. Hence, it may be that both depth and surface temperature play an important role in biogeographical differentiation. Biogeography also played a critical role in the evolutionary history of graptoloids. Important examples include the origination of axonophorans in deep, offshore environments from isograptid and pseudisograptid ancestors and their subsequent migration into shallow water regions; the replacement of the Diplograptina by Neograptina in the low palaeolatitudes during the Late Ordovician extinction event; and the origination of expansograptids in the ‘Atlantic’ Province as shallow water endemics followed by their worldwide dispersal into the oceanic biofacies.

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 score0.055
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.003

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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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