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Record W2950382733 · doi:10.1111/let.12336

Bioturbation in matgrounds at Lake Bogoria in the Kenya Rift Valley: implications for interpreting the heterogeneous early Cambrian seafloor

2019· article· en· W2950382733 on OpenAlexafffund
Jennifer J. Scott, Luís A. Buatois, M. Gabriela Mángano, Robin W. Renaut, R. Bernhart Owen

Bibliographic record

VenueLethaia · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversity of SaskatchewanMount Royal University
FundersNatural Sciences and Engineering Research Council of CanadaHong Kong Baptist University
KeywordsRift valleyGeologyRiftSeafloor spreadingPaleontologyBioturbationTrace fossilEcologyTectonicsBiologySediment

Abstract

fetched live from OpenAlex

Modern burrowing organisms feed on microbial organic matter in matgrounds near hot springs on the margins of Lake Bogoria, a saline alkaline lake in the Kenya Rift Valley. The burrowers produce a low-diversity trace assemblage similar to those produced by undermat miners during the Ediacaran–Cambrian transition. Despite obvious differences in body plans and phylogenetic affinities, these modern animals feed on microbes in similar ways to those inferred for primitive bilaterians. With increasing distance from hot-spring vents, outflow channels and adjacent matgrounds, the diversity and depth of the traces increase and mixgrounds become dominant. This modern extreme environment gives clues for interpreting the heterogeneous early Cambrian seafloor, with: (1) the restriction of ‘pre-agronomic revolution’ matground substrates; and (2) expansion of adjacent ‘post-agronomic revolution’ mixground areas.

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)0.000
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: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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