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Record W3200697076

Insecticidas que afectan algo más que insectos

2019· article· es· W3200697076 on OpenAlexaffabout
Edel Pérez‐López

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHumanitiesGeographyBiologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Los neonicotinoides son un grupo de insecticidas derivados de la nicotina que alcanzaron gran popularidad en los 90 y un amplio uso hasta años recientes, en los que el impacto negativo para las abejas y otros insectos beneficiosos se ha hecho visible. Este efecto negativo es la razón por la que los insecticidas neonicotinoides han sido prohibidos en Europa y en Estados Unidos, aunque otros países aún no tienen una posición clara sobre estos insecticidas. No obstante, esto podría estar a punto de cambiar, pues un estudio reciente, realizado por investigadores del Departamento de Biología de la Universidad de Saskatchewan, ha demostrado que el efecto negativo de los insecticidas neonicotinoides va más allá de afectar a los insectos, ya que también afectan a las aves migratorias. En dicho estudio, los investigadores alimentaron gorriones de corona blanca (Zonotrichia leucophrys) con granos tratados con Imidacloprid —un insecticida neonicotinoide de gran uso en Canadá— al inicio de su trayectoria migratoria, y luego fueron liberados. Usando una red de telemetría basada en señales de radio, las aves fueron rastreadas para determinar cuánto demoraban en llegar a sus áreas de reproducción.--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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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 routes2
Has abstractyes

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