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Record W2050911162 · doi:10.1073/pnas.0709381104

A universal driver of macroevolutionary change in the size of marine phytoplankton over the Cenozoic

2007· article· en· W2050911162 on OpenAlexaff
Zoe V. Finkel, J. Sebbo, Susanne Feist‐Burkhardt, Andrew J. Irwin, Miriam Katz, Oscar Schofield, J. R. Young, Paul G. Falkowski

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMount Allison University
FundersNational Science Foundation
KeywordsPhytoplanktonDinoflagellatePlanktonAbiotic componentBiologyMacroevolutionEcologyCenozoicPicoplanktonMacroecologyOceanographyCell sizePaleontologyGeologyNutrientSpecies richnessPhylogenetics

Abstract

fetched live from OpenAlex

The size structure of phytoplankton assemblages strongly influences energy transfer through the food web and carbon cycling in the ocean. We determined the macroevolutionary trajectory in the median size of dinoflagellate cysts to compare with the macroevolutionary size change in other plankton groups. We found the median size of the dinoflagellate cysts generally decreases through the Cenozoic. Diatoms exhibit an extremely similar pattern in their median size over time, even though species diversity of the two groups has opposing trends, indicating that the macroevolutionary size change is an active response to selection pressure rather than a passive response to changes in diversity. The changes in the median size of dinoflagellate cysts are highly correlated with both deep ocean temperatures and the thermal gradient between the surface and deep waters, indicating the magnitude and frequency of nutrient availability may have acted as a selective factor in the macroevolution of cell size in the plankton. Our results suggest that climate, because it affects stratification in the ocean, is a universal abiotic driver that has been responsible for macroevolutionary changes in the size structure of marine planktonic communities over the past 65 million years of Earth's history.

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)0.000
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 score0.031
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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

Citations121
Published2007
Admission routes1
Has abstractyes

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