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Record W2944268392 · doi:10.1038/s41597-019-0069-7

Worldwide measurements of bioturbation intensity, ventilation rate, and the mixing depth of marine sediments

2019· article· en· W2944268392 on OpenAlexaff
Martin Solan, Ellie R. Ward, Ellen L. White, Elizabeth E. Hibberd, Camilla Cassidy, Jasmin M. Schuster, Rachel Hale, Jasmin A. Godbold

Bibliographic record

VenueScientific Data · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsMemorial University of Newfoundland
FundersNatural Environment Research CouncilResearch Councils UKSight Research UK
KeywordsBioturbationBiogeochemical cycleEnvironmental scienceEcologyEcosystem engineerEcosystemBurrowOceanographySedimentPaleolimnologyIntertidal zoneMarine ecosystemClimate changeGeologyBiologyGeomorphology

Abstract

fetched live from OpenAlex

The activities of a diverse array of sediment-dwelling fauna are known to mediate carbon remineralisation, biogeochemical cycling and other important properties of marine ecosystems, but the contributions that different seabed communities make to the global inventory have not been established. Here we provide a comprehensive georeferenced database of measured values of bioturbation intensity (Db, n = 1281), burrow ventilation rate (q, n = 765, 47 species) and the mixing depth (L, n = 1780) of marine soft sediments compiled from the scientific literature (1864-2018). These data provide reference information that can be used to inform and parameterise global, habitat specific and/or species level biogeochemical models that will be of value within the fields of geochemistry, ecology, climate, and palaeobiology. We include metadata relating to the source, timing and location of each study, the methodology used, and environmental and experimental information. The dataset presents opportunity to interrogate current ecological theory, refine functional typologies, quantify uncertainty and/or test the relevance and robustness of models used to project ecosystem responses to 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Dataset
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Dataset
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.014
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.068
GPT teacher head0.275
Teacher spread0.207 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreDataset

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

Citations94
Published2019
Admission routes1
Has abstractyes

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