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Record W2242267994

Biogeochemical cycles of Chernobyl-born radionuclides in the contaminated forest ecosystems: long-term dynamics of the migration processes

2013· article· en· W2242267994 on OpenAlexaff
А. И. Щеглов, О. Б. Цветнова, Alexey Klyashtorin

Bibliographic record

VenueEuropean geosciences union general assembly · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsBiogeochemical cycleEnvironmental scienceEcosystemRadionuclideBiotaSoil waterPlant litterLitterForest ecologyRadioecologyTerrestrial ecosystemHydrology (agriculture)EcologySoil scienceGeologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Abstract The paper integrates the results of 25-year monitoring study of 137 Cs and 90 Sr biogeochemical cycle in the forest ecosystems of Russia and Ukraine contaminated due to the Chernobyl accident. The monitoring network was established in 1986 as a number of long-term key sites (KS) located 5 to over 500 km from the Chernobyl NPP. The following components have been monitored: biota (trees, grass and shrubs, mosses, and fungi), soils (forest litter and mineral horizons), soil water, and throughfall. Presently, 25 years after the Chernobyl fallout, 137 Cs and 90 Sr uptake by vegetation exceed their infiltration through the soil, i.e. biogeochemical cycle currently plays an important role impeding the radionuclide infiltration through soil to the ground water. In wet, accumulative landscapes, biota is a leading factor of the radionuclide cycle, while in dry, eluvial landscapes, soil absorbing complex plays a more important role. The effect of landscape type is manifested for 137 Cs, yet less important for 90 Sr. 137 Cs is actively uptaken by the fungi complex, while 90 Sr is primarily accumulated in the arboreal vegetation. Biogeochemical fluxes of 137 Cs and 39 K in some ecosystems are still different, even 25 years after the fallout.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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 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
Published2013
Admission routes1
Has abstractyes

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