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Record W3031730417 · doi:10.1073/pnas.1916923117

Past and future decline of tropical pelagic biodiversity

2020· article· en· W3031730417 on OpenAlexafffund
Moriaki Yasuhara, Chih‐Lin Wei, Michal Kučera, Mark J. Costello, Derek P. Tittensor, Wolfgang Kiessling, Timothy C. Bonebrake, Clay Tabor, Ran Feng, Andrés Baselga, Kerstin Kretschmer, Buntarou Kusumoto, Yasuhiro Kubota

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsDalhousie University
FundersMinistry of Science and Technology, TaiwanJarislowsky FoundationResearch Grants Council, University Grants CommitteeDeutsche Forschungsgemeinschaft
KeywordsPelagic zoneBiodiversityTropical AtlanticGeographyTropical marine climateEnvironmental scienceTropical climateEcologyOceanographyBiologyGeologySea surface temperatureMeteorology

Abstract

fetched live from OpenAlex

A major research question concerning global pelagic biodiversity remains unanswered: when did the apparent tropical biodiversity depression (i.e., bimodality of latitudinal diversity gradient [LDG]) begin? The bimodal LDG may be a consequence of recent ocean warming or of deep-time evolutionary speciation and extinction processes. Using rich fossil datasets of planktonic foraminifers, we show here that a unimodal (or only weakly bimodal) diversity gradient, with a plateau in the tropics, occurred during the last ice age and has since then developed into a bimodal gradient through species distribution shifts driven by postglacial ocean warming. The bimodal LDG likely emerged before the Anthropocene and industrialization, and perhaps ∼15,000 y ago, indicating a strong environmental control of tropical diversity even before the start of anthropogenic warming. However, our model projections suggest that future anthropogenic warming further diminishes tropical pelagic diversity to a level not seen in millions of years.

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 score0.030
Threshold uncertainty score0.236

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 science0.0000.001
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 head0.029
GPT teacher head0.241
Teacher spread0.212 · 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

Citations123
Published2020
Admission routes2
Has abstractyes

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