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Record W3084622233 · doi:10.4231/npyj-ge58

Data for Validation and Sensitivity Analysis of a 1-D Lake Model across Global Lakes

2020· article· en· W3084622233 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePurdue University Research Repository · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMinistry of Environment
Fundersnot available
KeywordsSensitivity (control systems)Environmental scienceClimatologyGeologyEngineering

Abstract

fetched live from OpenAlex

<p>This dataset includes the model calibration and validation results of 58 lakes using ALBM, and the corresponding sensitivity test results. Calib/ contains the metrics of water temperature simulations in the calibration and validation. CART/ contains the summaries of parameter sensitivity tests from Classification and Regression Tree (CART) model training. codes/ contains R codes for data analysis.</p> <p>Lakes have important influence on weather and climate from local to global scales. However, their prediction using numerical models is notoriously difficult because global lakes are highly heterogeneous across the globe and observations are sparse. Here, we assessed the performance of a 1-D lake model in simulating the thermal structures of 58 lakes with diverse morphometric and geographic characteristics by following the phase 2a local lake protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2a). The model was calibrated using six years of observation data for each lake and validated using the remaining data. After model calibration, the root-mean-square errors (RMSE) were below 2 °C for 70% and 75% of the lakes for epilimnion temperature and full-profile temperature simulations, with an average of 1.71 °C and 1.43 <a name="OLE_LINK1"></a><a name="OLE_LINK2">°C</a>, respectively. The model performance mainly depends on lake shape rather than location, supporting the possibility of grouping model parameters by lake shape for global applications. Furthermore, through machine-learning based parameter sensitivity tests, we identified turbulent heat fluxes, wind-driven mixing and water transparency as the major processes controlling lake thermal and mixing regimes. Snow density is also a sensitive parameter for modeling the ice phenology of high latitude lakes. The relative influence of the key processes and the corresponding parameters mainly depended on lake latitude and depth. Turbulent heat fluxes showed a decreasing importance in affecting lake epilimnion temperature with increasing latitude. Wind-driven mixing was less influential to lake vertical temperature profile for deeper lakes while the impact of light extinction, on the contrary, showed a positive correlation with depth on lake stratification. Our findings may guide improvements in 1-D lake model parameterizations to achieve higher fidelity in simulating global lake thermal dynamics. </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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.081
GPT teacher head0.325
Teacher spread0.244 · 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