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The Latitudinal Diversity Gradient

2023· reference-entry· en· W4304204513 on OpenAlexaff
Jonathan Rolland, Benjamin G. Freeman

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiological dispersalEcologyDiversity (politics)Extinction (optical mineralogy)Temperate climateTropicsBiologyGenetic algorithmFossil RecordBiodiversityGeographyEvolutionary biologyPaleontology

Abstract

fetched live from OpenAlex

The latitudinal diversity gradient describes the fact that there are less species in the temperate regions than in the tropics. The latitudinal diversity gradient is observed in most groups of animals, plants, and microorganisms, and remains one of the oldest and most famous mysteries in ecology. The ubiquity of this pattern suggests the possibility of a single general explanation for the latitudinal diversity gradient, and scientists since von Humboldt and Darwin have formulated dozens of hypotheses to explain the causes of this gradient. This article reviews the literature describing the main evolutionary hypotheses related to the fundamental processes that can explain why some places (like the tropics) have more species than others: speciation, extinction, colonization (dispersal), and the time necessary for diversity to accumulate. The recent advances in global-scale datasets of species distributions, the fossil record, and molecular mega-phylogenies give some hope of determining the primary cause(s) of the latitudinal diversity gradient.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.005

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 head0.064
GPT teacher head0.247
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2023
Admission routes1
Has abstractyes

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