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Record W4224091682 · doi:10.5597/lajam00268

River dolphins (Inia geoffrensis and Sotalia fluviatilis) in the Peruvian Amazon: habitat preferences and feeding behavior

2022· article· en· W4224091682 on OpenAlexaff
Amanda M. Belanger, Andrew Wright, Catalina Gómez, Jack D. Shutt, Kimberlyn Chota, Richard E. Bodmer

Bibliographic record

VenueLatin American Journal of Aquatic Mammals · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaDalhousie University
FundersUniversity of KentEarthwatch Institute
KeywordsAmazon rainforestHabitatFisheryChannel (broadcasting)GeographyFish <Actinopterygii>EcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

To estimate river dolphin habitat preference through density, as well as which habitats were preferred for feeding in the Pacaya- Samiria National Reserve, surveys were conducted during the high- to low-water season transition, from 2016 to 2018, in the channels, lakes, and confluences of the Samiria River. Both the Amazon river dolphin and tucuxi dolphin showed a preference for the confluences. The wide channel (Amazon: 24.8 dolphins/ km2, tucuxi: 7.6 dolphins/km2) and narrow channel (Amazon: 73.0 dolphins/km2; tucuxi: 6.0 dolphins/km2) also had high dolphin densities, especially for the Amazon river dolphins. In contrast with previous studies, the lakes had the lowest densities of dolphins for both species. High proportions of feeding behavior were observed in the confluence and wide channel habitats. The potentially larger presence of fish in these two habitats is likely the primary reason for the high dolphin densities. The high dolphin densities in the narrow channel, on the other hand, were associated with a low proportion of feeding behavior. Therefore, there are likely separate environmental factors attracting the dolphins, although additional data will be required to determine these factors. The results of this study will continue to help identify potential conservation and management actions by contributing to a better understanding of the ecology of river dolphins and their dependence on various habitats in one of the world’s largest protected flooded forests.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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