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Record W2065160043 · doi:10.1134/s1995425514040131

Experimental estimation of the possible use of submersed macrophytes for biotesting bottom sediments of the Yenisei River

2014· article· en· W2065160043 on OpenAlexaboutno aff
Т. А. Зотина, Е. А. Трофимова, A. Ya. Bolsunovsky, O. V. Anishenko

Bibliographic record

VenueContemporary Problems of Ecology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
Fundersnot available
KeywordsMacrophyteMyriophyllumElodea canadensisShootAquatic plantBiologyBotanyRadionuclideAgronomyHorticultureEcology

Abstract

fetched live from OpenAlex

The laboratory testing of bottom sediments (BSs) from the Yenisei River containing different concentrations of technogenic radionuclides, heavy metals, and biogenic elements (N and P) based on aquatic such plants as Elodea canadensis (Canadian waterweed) and Myriophyllum spicatum (Eurasian watermilfoil) has revealed a higher sensitivity of roots to the general quality of BSs than shoots: shoot length (9%) < root length (11%) < root number (15%) in M. spicatum; shoot length (22%) < root length (42%) < root number (44%) in E. canadensis. In contrast to M. spicatum, the growth parameters of roots and shoots in E. canadensis have differed in a significant statistical manner between most BS samples. A reverse correlation has been found between the increase in shoot length and the activity of technogenic radionuclides in BSs, which is mostly significant in E. canadensis (r 2 = 0.90–0.95, p = 0.05). Since the growth of shoots and roots in E. canadensis has turned out to be more sensitive to changes in the quality of BSs than that in M. spicatum, E. canadensis can be considered more prospective for biotesting BSs.

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 score0.047
Threshold uncertainty score0.248

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 studies0.0000.001
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 head0.037
GPT teacher head0.238
Teacher spread0.201 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

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