Bibliographic record
Abstract
Abstract. Hydrologic-land surface models (H-LSMs) provide physically-based understanding and predictions of the current and future states of the world’s vast high-latitude permafrost regions. Two major challenges, however, hamper their parametrization and validation when concurrently representing hydrology and permafrost. One is the high computational complexity, exacerbated by the need to include a deep soil profile to adequately capture the freeze/thaw cycles and heat storage. The other is that soil-temperature data are severely limited, and traditional model validation, based on streamflow, can show the right fit to these data for the wrong reasons. There are few observational sites for such vast, heterogeneous regions, and remote sensing provides only limited support. In light of these challenges, we develop 16 parametrizations of a Canadian H-LSM, MESH, for the sub-arctic Liard River Basin and validate them using three data sources: streamflows at multiple gauges, soil temperature profiles from few available boreholes, and multiple permafrost maps. The different parametrizations favor different sources of data and it is challenging to configure a model faithful to all three data sources, which are at times inconsistent with each other. Overall, the results show that: (1) surface insulation through snow cover primarily regulates permafrost dynamics after model initialization effects decay over, relatively long time and (2) different parametrizations yield different partitioning patterns of solid-vs-liquid soil-water and produce different low-flow but similar high-flow regimes. We conclude that, given data scarcity, an ensemble of model parametrizations is essential to provide a reliable picture of the current states and future spatio-temporal co-evolution of permafrost and hydrology.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.036 | 0.017 |
| Insufficient payload (model declined to judge) | 0.131 | 0.119 |
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".