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Record W2292645373

IMPACTOS Y REGULACIONES AMBIENTALES DEL ESTIÉRCOL GENERADO POR LOS SISTEMAS GANADEROS DE ALGUNOS PAÍSES DE AMÉRICA ENVIRONMENTAL REGULATIONS AND IMPACT OF MANURE GENERATED BY LIVESTOCK OPERATIONS IN SOME AMERICAN COUNTRIES

2012· article· es· W2292645373 on OpenAlexaboutno aff
Juan Manuel Pinos‐Rodríguez, Juan Carlos García-López, Luz Yosahandy Peña-Avelino, Juan Antonio Rendón-Huerta, Cecilia González-Gónzalez, Flor Tristán-Patiño

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsManureEnvironmental scienceManure managementEnvironmental protectionLivestockGreenhouse gasEnvironmental engineeringAgricultural scienceForestryGeographyAgronomyEcology
DOInot available

Abstract

fetched live from OpenAlex

Manure generated by livestock operations can cause negative impacts on the environment if there is no control of its storage, transport or application. Manure emits greenhouse gases into the atmosphere, and micro and macro nutrients accumulate in soil and bodies of surface water. Today, in the USA specific legislation exists for management and deposition of animal excreta that impact bodies of water, the soil and the atmosphere, which is supervised and certified by the Environmental Protection Agency (EPA). In Canada the regulations for management and deposition of animal manure are no less rigorous. In Argentina, Chile, Colombia and Mexico regulation and government vigilance of the use and management of animal mature is scarce and confusing, specifying only certain norms for discharges of pollutants into water, while giving less importance to emissions into the atmosphere and soil, and there are no clear specifications related to animal manure.

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)0.001
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 score0.142
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.247
Teacher spread0.240 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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