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Record W2403647438 · doi:10.60910/4emh-6f4w

Simulation of temperate freezing lakes by one-dimensional lake models : performance assessment for interactive coupling with regional climate models

2024· article· en· W2403647438 on OpenAlexfundno aff
Andrey Martynov, Laxmi Sushama, René Laprise

Bibliographic record

VenueTyöväentutkimus Vuosikirja · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersCanadian Foundation for Climate and Atmospheric SciencesUniversity of Wisconsin-MadisonNational Science Foundation
KeywordsTemperate climateSnowEnvironmental scienceAlbedo (alchemy)Climate modelHydrology (agriculture)ClimatologyGeologyPhysical geographyClimate changeAtmospheric sciencesEcologyOceanographyGeographyGeomorphology

Abstract

fetched live from OpenAlex

A systematic assessment of the ability of two selected 1-D lake models (the model of S.W. Hostetler and the Freshwater Lake model) to simulate lake surface temperature and fluxes for different lake conditions, corresponding to typical temperate freezing lakes of North America, through a set of offline tests, is presented.Results suggest that both models perform well in shallow lakes, while important differences between modelled and observed water temperatures and ice-cover duration can be noticed in deeper lakes.These differences could be partially attributed to the biases in the driving data and most importantly to the lack of representation of complex processes in the models, such as horizontal transfer of water and heat, ice drift, etc. Sensitivity of the models to lake depth, water transparency, explicit snow and snow/ice albedo is presented and possible ways of improving the performance of the 1-D lake models are proposed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.769

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 communication0.0000.001
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 head0.025
GPT teacher head0.255
Teacher spread0.230 · 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 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".

Quick stats

Citations114
Published2024
Admission routes1
Has abstractyes

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