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Record W2050262178 · doi:10.1007/s11745-003-1075-z

Fat, fishing patterns, and health among the bardi people of North Western Australia

2003· article· en· W2050262178 on OpenAlexafffund
Philippe Max Rouja, Éric Dewailly, Carole Blanchet

Bibliographic record

VenueLipids · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsCentre hospitalier universitaire de Québec
FundersUniversity of Guelph
KeywordsFisheryFishingFish <Actinopterygii>BiologyFleshResource (disambiguation)Turtle (robot)Zoology

Abstract

fetched live from OpenAlex

Research into the resource use strategies of the Bardi Aboriginal People of One Arm Point, Western Australia, found that they maximize the consumption of specific beneficial marine FA. The Bardi assess the relative fatness of fish and animal species in their environment, procuring fish and marine species only when they are considered to be at their fattest stage: during specific seasons; at specific physiological life stages, or through on-site evaluation. In June 1999 and September 2000, samples of fish, dugong, oyster, and turtle were collected by Bardi fishermen, focusing specifically on species considered to be high in fat content and very popular among the Bardi. Nine species were analyzed for total lipids and FA profile, which were determined by capillary GLC. Comparative lipid analysis established that the Bardi hunters' selection process between species and within species and the selection of specific fish fat deposits increase the levels of beneficial F made available to the community. Bardi fishing and hunting patterns meet a demand for fat within the community and may protect many species of fish whose spawning season is inversely related to the accumulation of the specific gut fat deposits sought by the Bardi. These fat deposits make up for the relatively low levels of fat in the flesh of tropical fish. The Bardi model provides important insights into the nature of human-environment interaction and expands our understanding of the role that warmer-water fisheries can play in human health.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.976

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)0.0010.000

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 head0.031
GPT teacher head0.263
Teacher spread0.232 · 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 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

Citations20
Published2003
Admission routes2
Has abstractyes

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