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Record W3089746995 · doi:10.1017/s0016756820000990

The Mesozoic Arctic: warm, green, and highly diverse

2020· article· en· W3089746995 on OpenAlexaff
Bas van de Schootbrugge, Gunn Mangerud, Jennifer M. Galloway, Sofie Lindström

Bibliographic record

VenueGeological Magazine · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsContent (measure theory)MesozoicThe arcticArcticChemistryGeologyOceanographyPaleontologyMathematics

Abstract

fetched live from OpenAlex

The Arctic is disproportionately affected by current and forecasted anthropogenic climate change (ACIA, 2005). As a consequence, sensitive ecosystems are suffering from rapid and enduring changes in, for example, sea ice volume, permafrost stability, precipitation, sea surface and air temperatures, and biodiversity Long time perspectives can help us to better understand the response of high northern latitude environments to the consequences of climate forcing in the future. Sediment and bedrock archives can inform us of past conditions of climate, sea level, and fauna and flora that existed and even thrived in Arctic regions despite the long polar night. The Mesozoic Era, in particular, offers a view into the fascinating world of the past, when dinosaurs and diverse forests existed in polar regions during a time typically thought of as a climatic greenhouse. During the Mesozoic, atmospheric CO 2 concentrations were much higher than today, over 1000 ppm (as compared to the pre-industrial average of 280 ppm). Sea-surface temperatures may have exceeded 32C at 15-20N, while averaging 26C at 53S (Littler et al. 2011). Thus, looking to the past offers the potential for insight into what the planet may look like in the future if greenhouse gas emissions continue unabated.

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 score0.168
Threshold uncertainty score0.631

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)0.0010.000

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 head0.026
GPT teacher head0.214
Teacher spread0.188 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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