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Record W4409717678 · doi:10.1371/journal.pone.0317424

Photosynthetic oxygen bubble stream sounds from aquatic macrophytes, and their consequences for acoustic biodiversity inventories and acoustic communication in shallow freshwater settings

2025· article· en· W4409717678 on OpenAlexaffabout
Keith G. Campbell, Yelena Cerezke-Riemer, John Acorn

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSoundscapeMacrophyteBiodiversityBiologyWaves and shallow waterEcologyEcosystemEnvironmental scienceFreshwater ecosystemSound (geography)OceanographyGeology

Abstract

fetched live from OpenAlex

The emerging field of soundscape ecology focuses on biological, geophysical, and anthropogenic sounds, and provides a non-invasive method to inventory ecosystems. Most of the work on freshwater soundscapes focuses on larger fishes in deeper water, or on insects. We suggest the possibility that such studies have either missed or misidentified photosynthetic oxygen bubble sounds (POBS) produced by bubble streams from damaged macrophytes in sunny shallow water. These contribute significantly to local soundscapes. We recorded such sounds in the shallows of Gull Lake, Alberta, Canada, where POBS from sago pondweed (Stuckenia pectinata), along with water boatman stridulations (Hemiptera: Corixidae), comprised almost all of the sounds we encountered. These sounds attenuate rapidly with distance, and the POBS constitute a remarkable acoustic diversity, resulting in a patchwork of very different soundscapes in these shallows. Recognition of POBS has important consequences for acoustic bioinventories in shallow water, rapid ecosystem assessments involving indices of primary production, and bioacoustics studies of such organisms as corixid bugs, communicating against a cacophonous background of POBS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.239
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2025
Admission routes2
Has abstractyes

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