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Record W4281780527 · doi:10.1016/j.ecoenv.2022.113706

Application of Non-invasive Micro-test Technology (NMT) in environmental fields: A comprehensive review

2022· review· en· W4281780527 on OpenAlexaff
Mengxuan Han, Huan Yang, Guo Yu, Pingping Jiang, Shaohong You, Lei Zhang, Hua Lin, Jie Liu, Yi Shu

Bibliographic record

VenueEcotoxicology and Environmental Safety · 2022
Typereview
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Alberta
FundersScience and Technology Major Project of GuangxiNational Natural Science Foundation of China
KeywordsPhytoremediationEnvironmental scienceMicroelectrodeField (mathematics)Biochemical engineeringComputer scienceHeavy metalsNanotechnologyEnvironmental chemistryMaterials scienceChemistryEngineering

Abstract

fetched live from OpenAlex

Non-invasive Micro-test Technology (NMT) is a selective microelectrode technique which can detect the flux rates and three-dimensional motion directions of ions or molecules into and out of living organisms in situ without damaging the sample. It has the advantages of maintaining sample integrity, high temporal and spatial resolution, and being able to measure multiple sites simultaneously. In this paper we provide a comprehensive review on the development of NMT in recent years. Its principles, characteristics, and the differences with other microelectrode techniques are introduced. We discuss the applications of NMT in the field of phytoremediation, plant resistance, water quality monitoring, and toxicity mechanisms of heavy metals on organisms. Furthermore, the challenges and future prospects of NMT in the environmental field are presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.009
GPT teacher head0.246
Teacher spread0.238 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2022
Admission routes1
Has abstractyes

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