A Spatially Parallel Implementation of a Lake and Land
Bibliographic record
Abstract
Canada has several of Earth's largest lakes and many small lakes. Heat storage and circulation are greatly affected by lakes. Currently the Canadian Regional climate model does not incorporate a lake component. Therefore, we are linking atmospheric and lake models for such applications as climate prediction and assessing changes in the lake water quality and quantity. We investigate use of highly parallel arrays of clustered processors, available through Canada's SHARCNET. The accuracy of lake, land and atmospheric models depends on grid spacing. Coarser grids adversely affect accuracy. Regional climate model inputs are required subhourly, placing a lower bound on the grid sizes that can be employed. We link a one-dimensional lake model such as the Dynamic Reservoir Model (DYRESM) to a Regional Climate model (RCM) to incorporate the effects of lake on the regional climate. The land model used is the Canadian Land Surface Scheme (CLASS). CLASS and DYRESM are vertical models, with no interaction between horizontally neighboring nodes. CLASS computes heat and moisture fluxes for bare ground (fractional coverage by ground, FG), snow-covered ground (fractional coverage by snow, FSN), ground with canopy (fractional coverage by ground, FC), and ground with both snow and canopy. These fractions are combined to calculate node characteristics. Lake flux values are provided by DYRESM, which are combined with land values according to the fractional lake coverage. Hybrid model is designed and implemented using a mix of both serial farm and task parallel approaches on Guelph SHARCNET high performance computing cluster.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".