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Record W2912368391 · doi:10.5194/gmd-12-3045-2019

Incorporating wind sheltering and sediment heat flux into 1-D models of small boreal lakes: a case study with the Canadian Small Lake Model V2.0

2019· article· en· W2912368391 on OpenAlex
Murray Mackay

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeoscientific model development · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsFlux (metallurgy)Environmental scienceClimate modelAtmosphere (unit)ClimatologySedimentSensible heatWind speedBorealMixing (physics)Heat fluxAtmospheric sciencesClimate changeMeteorologyGeologyOceanographyHeat transferGeographyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. Lake models are increasingly being incorporated into global and regional climate and numerical weather prediction systems. Lakes interact with their surroundings through flux exchange at their bottom sediments and with the atmosphere at the surface, and these linkages must be well represented in fully coupled prognostic systems in order to completely elucidate the role of lakes in the climate system. In this study schemes for the inclusion of wind sheltering and sediment heat flux simple enough to be included in any 1-D lake model are presented. Example simulations with the Canadian Small Lake Model show improvements in surface-wind-driven mixing and temperature in summer and a reduction of the bias in the change in heat content under ice compared with a published simulation based on an earlier version of the model.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.220
Teacher spread0.186 · 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