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Record W7067385465

Mamíferos del departamento de Córdoba-Colombia: historia y estado de conservación

2015· other· es· W7067385465 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typeother
Languagees
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNatural historyWildlifeNatural (archaeology)Field (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

Se presenta el listado taxonómico de los mamíferos presentes en el departamento de Córdoba. Fueron revisados datos de colecciones científicas de referencia de museos nacionales y extranjeros, para lo cual se incluyen resultados de investigaciones de los grupos de investigación Biodiversidad Unicórdoba (Universidad de Córdoba) y del Laboratorio de Ecología Funcional-Unidad de Ecología y Sistemática (UNESIS) (Pontificia Universidad Javeriana). Se revisaron las bases de datos de 13 museos de Norteamérica (MaNIS), además de los registros encontrados en el Smithsonian National Museum of Natural History (NMNH), Royal Ontario Museum (ROM), Texas Cooperative Wildlife Collection (TCWC) y USA Field Museum of Natural History (FMHN). Los mamíferos del departamento de Córdoba están representados por 40 familias, 90 géneros y 117 especies. La mayor riqueza se presenta en las subregiones del alto Sinú y San Jorge, con especies del orden Chiroptera (45,3 %), seguido de Carnivora (12 %), Rodentia (10,3 %) y Cetacea (9,4 %).

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.840

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.266
GPT teacher head0.464
Teacher spread0.197 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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