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Record W2054547842 · doi:10.3137/ao.400402

Implications during transitional periods of improvements to the snow processes in the land surface scheme ‐ hydrological model WATCLASS

2002· article· en· W2054547842 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueATMOSPHERE-OCEAN · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSnowpackSnowSnowmeltEnvironmental scienceInterceptionStreamflowPrecipitationHydrology (agriculture)Water contentAlbedo (alchemy)Atmospheric sciencesMeteorologyGeologyGeographyDrainage basin

Abstract

fetched live from OpenAlex

Abstract The representation of snow processes is crucial in both hydrological models and land surface schemes. The importance of the detailed physical representation of four snow processes in the WATCLASS hydrological‐ land surface scheme model is examined. The snow processes are: the occurrence of mixed precipitation; the density of fresh snow; the maximum snowpack density; and canopy snowfall interception. It is shown that the inclusion of the non‐static processes does not significantly improve the simulated streamflow. The changes in the simulation of state variables, in particular, the snowpack depth, snow water equivalent, soil temperature and soil moisture content are small, but may become important during transitional periods, such as the initial accumulation and depletion of snow‐covered areas during snowmelt. This substantially alters the surface heat fluxes during these periods.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.791

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)0.0010.000

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 head0.028
GPT teacher head0.223
Teacher spread0.195 · 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