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Record W4312660056 · doi:10.15406/jabb.2022.09.00295

Effect of sewage polluted by heavy metal on domestic crops

2022· article· en· W4312660056 on OpenAlexaboutno aff
Delfino Marín-Mendoza, Gabriel Gallegos‐Morales, Jesus Jaime Hernández-Escareño, Juan Manuel Sánchez–Yáñez

Bibliographic record

VenueJournal of Applied Biotechnology & Bioengineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersDirectorate for Biological SciencesUniversidad Michoacana de San Nicolás de HidalgoUniversidad Autónoma de Nuevo León
KeywordsEnvironmental scienceAgricultureIrrigationSewageAgronomyYield (engineering)CropHeavy metalsGreenhouseEnvironmental protectionEnvironmental engineeringBiologyChemistryEcologyEnvironmental chemistry

Abstract

fetched live from OpenAlex

The application of sewage water (SW) to irrigate and feed agricultural crops in Mexico is widespread due to the problem of scarcity in the country, and in the world. Agricultural crops not eaten raw irrigated and fed with SW benefit and/or harm directly or indirectly by the chemical composition that in industrial cities that includes heavy metals of risk to human health. The objective of this work was to analyze the effect of sewage with heavy metals on the growth and yield of an agricultural soil of "El Canada" N.L. Mexico. Compared with plant growth and yield in a soil from Cadereyta, Nuevo León, Mexico in greenhouse irrigated with ground water and conventional chemical fertilization. For which the concentration of heavy metals of the SW in the plant and the soil was determined. The results show that the SW used in the irrigation of agricultural crops supported plant growth. With no evidence that heavy metals caused any negative effect on plant growth and yield, despite the length of SW use, it is believed that some of these crops have developed tolerance to heavy metals regarding the risk to be consume by humans and animals

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.002
GPT teacher head0.188
Teacher spread0.186 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

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