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Record W3155253887 · doi:10.35567/1999-4508-2015-1-5

Effects of the Urea and Heavy Metals (Ni2+ and Cu2+) Higher Concentrations on Aquatic Macrophytes (Egeria densa Planch. as a Study Case)

2015· article· en· W3155253887 on OpenAlexaboutno aff

Bibliographic record

VenueWater sector of Russia problems technologies management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMacrophyteAquatic plantUreaChemistryBotanyHydrocharitaceaePhotosynthesisLipid peroxidationHatchingEnvironmental chemistryBiologyAntioxidantAnimal scienceEcologyBiochemistry

Abstract

fetched live from OpenAlex

Urea and heavy metals (Ni2+ and Cu2+) higher concentration infl uence on the photosynthetic pigments’ content, lipids peroxidation intensity and urease ferments activity in the submerged aquatic macrophyte – Egeria densa Planch. have been studied. It has been shown that addition of metals to the medium with urea tends to strengthen its toxic effect upon plants. It has been found that four-day long hatching of plants in the medium with urea and nickel lead to the oxidative stress development in Canada water weed leaves. Copper concentration equal to 100 mkmol/l appeared to be lethal for plants.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.205
Teacher spread0.195 · 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 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
Published2015
Admission routes1
Has abstractyes

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