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Record W3199415932

Nutrientes desde el cielo: la curiosa dieta de los tiburones tigre

2019· article· es· W3199415932 on OpenAlexaff
Edel Pérez‐López

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019
Typearticle
Languagees
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeographyHumanitiesZoologyEcologyBiologyArt
DOInot available

Abstract

fetched live from OpenAlex

Los tiburones tigre (Galeocerdo cuvier) son una de las tantas criaturas que habitan los mares tropicales. Como depredadores marinos, los tiburones tigre cazan sus presas de manera activa, incluyendo crustáceos, peces, serpientes, tortugas, aves y mamíferos, pero además, estos animales son carroñeros facultativos, lo que les permite complementar la dieta con restos de animales muertos, tales como restos de ballenas. ¿Pero quién imaginaría que los tiburones tigre también se alimentan de aves terrestres? Pues un estudio reciente publicado en la revista Ecology demostró que los tiburones tigre colectados en la costa de Mississippi-Alabama, Estados Unidos, en el Golfo de México, se han estado alimentando de aves terrestres tales como golondrinas (Hirundo rustica), tiránidos (Tyrannus tyrannus), sotorreyes (Troglodytes aedon), parúlidos (Geothlypis trichas), ictéridos (Sturnella magna), gorriones (Melospiza georgiana), palomas (Zenaida asiatica) y carpinteros (Sphyrapicus varius), entre otras —hasta once especies de aves terrestres en total—.--LEER MÁS--

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)0.000
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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.251
Teacher spread0.234 · 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
Published2019
Admission routes1
Has abstractyes

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