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Marine Metazoan Modern Mass Extinction: Improving Predictions by Integrating Fossil, Modern, and Physiological Data

2018· review· en· W2890426006 on OpenAlexafffund
Piero Calosi, Hollie M. Putnam, Richard J. Twitchett, Fanny Vermandele

Bibliographic record

VenueAnnual Review of Marine Science · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Rimouski
KeywordsMarine ecosystemBiodiversityExtinction (optical mineralogy)Extinction eventEcosystemEcologyGlobal changeSustenanceMarine lifeMarine biodiversityOcean acidificationBiologyClimate changePaleontology

Abstract

fetched live from OpenAlex

Evolution, extinction, and dispersion are fundamental processes affecting marine biodiversity. Until recently, studies of extant marine systems focused mainly on evolution and dispersion, with extinction receiving less attention. Past extinction events have, however, helped shape the evolutionary history of marine ecosystems, with ecological and evolutionary legacies still evident in modern seas. Current anthropogenic global changes increase extinction risk and pose a significant threat to marine ecosystems, which are critical for human use and sustenance. The evaluation of these threats and the likely responses of marine ecosystems requires a better understanding of evolutionary processes that affect marine ecosystems under global change. Here, we discuss how knowledge of ( a) changes in biodiversity of ancient marine ecosystems to past extinctions events, ( b) the patterns of sensitivity and biodiversity loss in modern marine taxa, and ( c) the physiological mechanisms underpinning species' sensitivity to global change can be exploited and integrated to advance our critical thinking in this area.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.016
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 head0.051
GPT teacher head0.321
Teacher spread0.269 · 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

Citations46
Published2018
Admission routes2
Has abstractyes

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