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Record W4206605103 · doi:10.1038/s41529-022-00216-6

Bioerosion of siliceous rocks driven by rock-boring freshwater insects

2022· article· en· W4206605103 on OpenAlexaff
Ivan N. Bolotov, Alexander V. Kondakov, Grigory S. Potapov, Dmitry M. Palatov, Nyein Chan, Zau Lunn, Galina V. Bovykina, Yulia Chapurina, Yulia S. Kolosova, Elizaveta A. Spitsyna, Vitaly M. Spitsyn, Artyom A. Lyubas, Mikhail Y. Gofarov, Ilya V. Vikhrev, V. O. Yapaskurt, A. Yu. Bychkov, Oleg S. Pokrovsky

Bibliographic record

Venuenpj Materials Degradation · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of New Brunswick
FundersRussian Science Foundation
KeywordsMayflyBioerosionFreshwater bivalveGeologyBiologyEcologyPaleontologyLarvaReefMolluscaBivalvia

Abstract

fetched live from OpenAlex

Abstract Macrobioerosion of mineral substrates in fresh water is a little-known geological process. Two examples of rock-boring bivalve molluscs were recently described from freshwater environments. To the best of our knowledge, rock-boring freshwater insects were previously unknown. Here, we report on the discovery of insect larvae boring into submerged siltstone (aleurolite) rocks in tropical Asia. These larvae belong to a new mayfly species and perform their borings using enlarged mandibles. Their traces represent a horizontally oriented, tunnel-like macroboring with two apertures. To date, only three rock-boring animals are known to occur in fresh water globally: a mayfly, a piddock, and a shipworm. All the three species originated within primarily wood-boring clades, indicating a simplified evolutionary shift from wood to hardground substrate based on a set of morphological and anatomical preadaptations evolved in wood borers (e.g., massive larval mandibular tusks in mayflies and specific body, shell, and muscle structure in bivalves).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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