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Record W4220688239 · doi:10.5194/egusphere-egu22-1235

Modeling the depth dependence of Cs-137 concentration in Lake Onuma 

2022· preprint· en· W4220688239 on OpenAlexaff
Yuko Hatano, Kentaro Akasaki, Eiichi Suetomi, Yukiko Okada, Kyuma Suzuki, Shun Watanabe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsParticulatesPlanktonChemistryAnimal scienceBiologyEcology

Abstract

fetched live from OpenAlex

<p>Lake Onuma on Mt. Akagi (Gunma Prefecture, Japan) is a closed lake with an average water residence time of 2.3 years. The activity concentration of radioactive cesium in the lake was high shortly after the Fukushima accident. According to Suzuki et al. [1] and Watanabe [2], after a filtration process, Cs-137 are separated into two groups: particulate form and dissolved form. These two forms appears to have very different concentration profiles with each other,  when the Cs-137 concentration plotted against the sampled water depths. In the present study, we are going to model those behavior of particulate/dissolved forms with an emphasis on the depth dependency.</p><p>We consider a creation-annihilation process of plankton for the model of the particulate form, since diatom shells are found to be a major constituent of the particulate Cs-137 [2]. We set  ∂P/∂t = f(x,t)  and  f(x,t) = χ(x) cos(ωt) (0 ≤ x ≤ L(water column height), t > 0),  where P=P(x,t) is the activity concentration of the particulate form. The term f(x,t) is the rate of the net production of the plankton at a specific location x at a specific time t. Seasonal cycle is also taken into account by the cosine function (we neglect the phase shift here). The function χ(x), depends solely on water depth x, is responsible for dynamics or inhomogeneity of lake water, such as circulation, stratification or a thermocline. We assume that such a water structure relates to the production rate of plankton through the function χ(x). Thus, we may obtain the concentration of particulate Cs-137. For the dissolved concentration S(x,t), we use the classical diffusion equation with the diffusivity K being dependent on both space and time (i.e. K(x,t)), namely ∂S/∂t =  ∇•(K(x,t) ∇S). Here S=S(x,t) is the activity concentration of the dissolved form. The total activity concentration C(x,t) is the sum of P(x,t) and S(x,t). Using the pair of the equations, we can reproduce the followings. (1) depth profiles of each of the soluble- and particulate activity concentration and (2) depth profiles of the total Cs-137 concentration.</p><p> [1] Suzuki, K. <em>et al.</em>,<em> Sci. Tot. Env.</em> (2018)</p><p> [2] Watanabe, S. <em>et al.</em>,  <em>Proc. 20<sup>th</sup> Workshop on Environmental Radioactivity </em>(2019)</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.990

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)0.0110.000

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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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.

Study designSimulation or modeling
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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