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Record W2442568315 · doi:10.15446/caldasia.v38n1.57833

CARACTERIZACIÓN DE LA ACUMULACIÓN DE CARBONO EN PEQUEÑOS HUMEDALES ANDINOS EN LA CUENCA ALTA DEL RÍO BARBAS (QUINDÍO, COLOMBIA)

2016· article· es· W2442568315 on OpenAlexaff
María Cecilia Roa-García, Sandra Brown

Bibliographic record

VenueCaldasia · 2016
Typearticle
Languagees
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeographyHumanitiesArt

Abstract

fetched live from OpenAlex

<p>Se caracterizó el proceso de acumulación de carbono en pequeños humedales andinos de la cuenca alta del río Barbas, en el municipio de Filandia, departamento del Quindío, Colombia. El método de bolsas de descomposición se usó para estimar las tasas de acumulación de materia orgánica y las constantes de descomposición en el corto plazo. La pérdida por ignición y la datación con carbono 14 se usaron para calcular las tasas de acumulación de carbono en el largo plazo. Los 52 humedales ocupan un área de 7,2 ha y se encuentran entre 2000 y 2200 m. La constante de descomposición, k es de 0,524 para los suelos de humedal en contraste con 0,962 para los suelos de tierra firme. La concentración de carbono en los tres humedales muestreados varía entre 91 y los 319 gr C Kg-1. Las reservas de carbono por unidad de área en los tres humedales muestreados varía entre 80 y 117 Kg C m-2. La tasa de acumulación de carbono en el largo plazo - LARCA - es de 30 - 50 gr C m-2 año-1, tasa comparable con la encontrada en turberas de páramos andinos.</p>

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.832

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)0.0010.000

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 head0.006
GPT teacher head0.254
Teacher spread0.248 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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