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Record W2897907155 · doi:10.1121/1.5067524

Sounds from the Amazon: Piranha and prey

2018· article· en· W2897907155 on OpenAlexaff
Rodney A. Rountree, Francis Juanes

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTributaryAmazon rainforestSoundscapePredationSound (geography)Sound productionFisheryUnderwaterNatural soundsBiologyEnvironmental scienceEcologyGeographyAcousticsOceanographyGeologyArchaeologyCartography

Abstract

fetched live from OpenAlex

The underwater soundscape of an upper tributary of the Amazon River was studied in a four-week survey from 3 to 26 July 2012 within the Pacaya-Samiria National Reserve, Peru. Over 550 individuals representing over 70 species of fishes were auditioned for sound production. In addition, over 641 minutes of natural sounds from the river were recorded from 22 sites. We demonstrate that closely related species of piranha can be distinguished by their hand-held disturbance sounds. Similar piranha sounds were recorded in the wild at locations where piranha were known to be actively feeding. Sounds of catfishes and other fishes were significantly more frequent at piranha feeding sites. Thus, piranha sounds appear to be excellent indicators of local piranha feeding activity and suggest that passive acoustic monitoring (PAM) can be an effective tool for studies on piranha behavior, feeding activity, and impact on prey fishes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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