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Record W2992486607 · doi:10.1002/lol2.10133

Capacity of the common Arctic picoeukaryote <i>Micromonas</i> to adapt to a warming ocean

2019· article· en· W2992486607 on OpenAlexafffund
Ina Benner, Andrew J. Irwin, Zoe V. Finkel

Bibliographic record

VenueLimnology and Oceanography Letters · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsMount Allison University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhytoplanktonGrowth rateArcticEnvironmental scienceEffects of global warming on oceansGlobal warmingClimate changeIrradianceEcologyAtmospheric sciencesOceanographyBiologyNutrientMathematicsPhysicsGeology

Abstract

fetched live from OpenAlex

Abstract Phytoplankton are sensitive to temperature and other environmental conditions expected to change with warming over the next century. We quantified the capacity of an ecologically dominant Arctic phytoplankton species, Micromonas polaris , to adapt to changes in temperature, increased temperature and irradiance, and increased temperature and periodic nitrogen starvation, over several hundred generations. When originally isolated, this strain of Micromonas had its maximum growth rate at 6°C, and its growth rate declined above 10°C. We find an evolutionary increase in growth rate, with the largest increases associated with the elevated temperature treatments, especially when combined with repeated nitrate starvation. After several hundred generations of exposure, the growth rate of Micromonas under 13°C almost doubled and was higher than under 6°C. This increase in growth rate is consistent with the Arrhenius model of temperature effects on metabolism and suggests a general hypothesis for the evolutionary potential of phytoplankton to respond evolutionarily to temperature change.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.487

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)0.0000.000

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 head0.005
GPT teacher head0.195
Teacher spread0.191 · 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

Citations20
Published2019
Admission routes2
Has abstractyes

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