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Population, density estimates, and conservation of river dolphins (<i>Inia</i>and<i>Sotalia</i>) in the Amazon and Orinoco river basins

2011· article· en· W2094143838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMarine Mammal Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
FundersInnovative Research Group Project of the National Natural Science Foundation of China
KeywordsTributaryAmazon rainforestGeographyPopulationFisheryHabitatCetaceaTransectDrainage basinPopulation densityMain riverEcologyBiologyCartography

Abstract

fetched live from OpenAlex

Abstract This study is part of an on‐going effort to evaluate and monitor river dolphin populations in South America. It comprises the largest initiative to estimate population size and densities of Inia and Sotalia dolphins using statistically robust and standardized methods. From May 2006 to August 2007, seven visual surveys were conducted in selected large rivers of Bolivia, Colombia, Brazil, Ecuador, Peru, and Venezuela in the Amazon and Orinoco river basins. Population sizes of Inia and Sotalia were estimated for different habitats (main river, tributary, lake, island, confluence, and channel). A total of 291 line and 890 strip transects were conducted, covering a distance of 2,704 linear kilometers. We observed 778 Inia geoffrensis , 1,323 Inia boliviensis , and 764 Sotalia fluviatilis . High‐density areas were identified (within 200 m from the river banks, confluences, and lakes) and we propose that these constitute critical habitat for river dolphins. High densities of river dolphins seem to coincide with well‐managed freshwater protected areas and should be considered as hot spots for river dolphins in South America.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.996

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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