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Record W2332766197 · doi:10.14237/ebl.7.1.2016.572

Eastern Sumbanese Bird Classification and Nomenclature: Additions and Revisions

2016· article· en· W2332766197 on OpenAlexaff
Gregory Forth

Bibliographic record

VenueEthnobiology Letters · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMediterranean and Iberian flora and fauna
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTaxonNomenclatureTaxonomy (biology)CovertGeographyGenealogyHistoryClassification schemeEthnologyZoologyArchaeologyEcologyBiologyLinguisticsInformation retrievalComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Expanding on previously published research into folk classification of birds in the eastern part of the Indonesian island of Sumba, this article reports new information on bird categories and classification recorded by the author in 2015. New folk taxa are described and identified and scientific identifications for previously reported taxa are added or revised. Information on local bird classification from the Kambera region is compared with data recorded in the 1970s in the district of Rindi, and these are shown to reveal only minor differences. Employing Berlin’s well-known analytical scheme, the main features of the taxonomy are summarized and totals are enumerated for monotypic and polytypic folk-generics and both named and unnamed (covert) folk-intermediates. Additional information is provided concerning symbolic uses of bird categories, including bird names used as place names and local beliefs about nightjars (Camprimulgus spp.) which correspond to ideas encountered on the ethnozoologically better-known neighboring island of Flores.

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.009
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.028
GPT teacher head0.215
Teacher spread0.186 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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