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Record W4389105053 · doi:10.21425/f5fbg61673

The universal evolutionary and ecological significance of 20 oC

2023· article· en· W4389105053 on OpenAlexfundno aff
Mark J. Costello, Ross Corkrey, Amanda E. Bates, Michael T. Burrows, Chhaya Chaudhary, Graham E. Edgar, Rick D. Stuart‐Smith, Moriaki Yasuhara, Chih‐Lin Wei

Bibliographic record

VenueFrontiers of Biogeography · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
FundersJoint Research CentreUniversity of Hong KongMinistry of Science and Technology, TaiwanCanada Research Chairs
KeywordsPelagic zoneSpecies richnessBenthic zoneEcologyBiologyBiodiversityInvertebratePlanktonMacroecologyExtinction (optical mineralogy)

Abstract

fetched live from OpenAlex

We connect evidence that 20 oC is the most stable temperature for cellular processes with macroecological observations. Examples show that temperatures warmer than ~20 oC result in decreases in: aquatic species’ tolerance to low oxygen; marine pelagic and benthic algal productivity; pelagic and benthic predation rates; global species richness in pelagic fishes, plankton and benthic invertebrates; and genetic diversity; but increased extinctions in the fossil record. The realised thermal niche of reef fishes and invertebrates globally is narrowest among species with distributions centred on 20 oC, as also seen in microbes. While many species have evolved to live at warmer and colder temperatures, most species live at, and extinctions in the fossil record across seven phyla were lower at, 20 oC. The mathematical “Corkrey” model, which predicts that thermal breadth should be minimized and species richness maximised at 20 oC across all Domains of life, provides an explanation for this “20 oC effect”. A literature search found highest species richness at ~20 oC across life in air and water, including animals, plants and microbes. That life seems centred around ~20 oC implies fundamental constraints that compromise the ability of extant tropical species to adapt to higher temperatures.

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 score0.519
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.186
Teacher spread0.179 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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