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Record W3152215194 · doi:10.1101/2021.04.03.438320

The global ocean size-spectrum from bacteria to whales

2021· preprint· en· W3152215194 on OpenAlexaff
Ian Hatton, Ryan Heneghan, Yinon M. Bar‐On, Eric D. Galbraith

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiomass (ecology)Marine lifeGeneralityPlanktonScale (ratio)LogarithmBiologyEcologyRange (aeronautics)Environmental scienceMathematicsGeographyCartographyPsychology

Abstract

fetched live from OpenAlex

Abstract It has long been hypothesized that aquatic biomass is evenly distributed among logarithmic body mass size-classes. Although this community structure has been observed locally among plankton groups, its generality has never been formally tested across all marine life, nor have its impacts by humans been broadly assessed. Here, we bring together data at the global scale to test the hypothesis from bacteria to whales. We find that biomass within most order of magnitude size-classes is indeed remarkably constant, near 1 Gt wet weight (10 15 grams), but that bacteria and whales are markedly above and below this value, respectively. Furthermore, human impacts have significantly truncated the upper one-third of the spectrum. Size-spectrum theory has yet to provide an explanation for what is possibly life’s largest scale regularity. One Sentence Summary Human activities have fundamentally altered one of life’s largest scale patterns; a global power law size distribution spanning bacteria to whales.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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 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

Citations14
Published2021
Admission routes1
Has abstractyes

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