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Record W4385820550 · doi:10.15381/iigeo.v25i50.22990

Origen de las aguas subterráneas en el acuífero Ica-Perú, basado en isotopos ambientales

2022· article· es· W4385820550 on OpenAlexaff
J Silvestre, David N. Bethune, James Emiliano Apaestegui Campos, M. Cathryn Ryan

Bibliographic record

VenueRevista del Instituto de investigación de la Facultad de minas metalurgia y ciencias geográficas · 2022
Typearticle
Languagees
FieldEnvironmental Science
TopicWater Resource Management and Quality
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHumanitiesGeologyGeographyCartographyPhilosophy

Abstract

fetched live from OpenAlex

El valle de Ica, ubicado en la zona central y occidental de Perú, es el principal valle con potencial agroexportador por sus altos índices de productividad y calidad, a la vez el acuífero del cual se extrae agua se enfrenta a una extracción intensiva de agua subterránea; dando lugar a un descenso considerable de los niveles piezométricos a causa de la sobreexplotación del acuífero. Hemos planteado un modelo conceptual para establecer la interconexión entre las precipitaciones ocurridas en la cuenca alta y el acuífero Ica, así como los mecanismos de recarga. Adicionalmente se ha establecido una red de monitoreo isotópico y geoquímico en puntos estratégicos desde la cuenca baja, media y alta, para la evaluación de aguas subterráneas, superficiales y de precipitaciones, esto ha permitido entender que el Acuífero Ica es recargado principalmente por agua proveniente de elevaciones medias y altas de la cuenca y es en estos puntos donde se debe hacer la recarga artificial mediante infiltración; ya que existe una interconexión directa a través de fallas geológicas entre el agua proveniente de altas elevaciones y del mismo acuífero.

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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.291
Teacher spread0.274 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevista del Instituto de investigación de la Facultad de minas metalurgia y ciencias geográficasSame topicWater Resource Management and QualityFrench-language works237,207