Modeling snowpack and soil temperature and moisture conditions in a jack pine, black spruce and aspen forest stand in central Saskatchewan (BOREAS SSA)
Bibliographic record
Abstract
Impacts of climate change on above- and below-ground heat and moisture conditions were modeled so that other impacts on, e.g., local carbon (C) and C-based pools for nutrients and pollutants such as Hg can be predicted reliably. This paper shows how the 199–-2003 data for the jack pine (jp; Pinus banksiana Lamb.), black spruce (bs; Picea mariana) and aspen (ta; Populus tremuloides) sites of the Southern Study Area of the BOREAS project were used to estimate some of the hydrothermal soil responses at these locations to daily variations in precipitation and air temperature. This was done by initializing and calibrating a forest hydrology model that has the capacity to simulate flow and retention of moisture and heat, as modified by canopy closure, ground cover, forest-floor depth, and soil composition. The calculations and data revealed strong but predictable site-specific differences in soil temperature and frost penetration (jp: 1–2 m > ta: 0.5–1 m > bs: 0–0.5 m), in soil moisture freezing (ta < bs < jp), and in moisture retention (jp < ta < bs). Apart from daily weather, these differences depended on soil texture (loamy/sandy texture impeded/encouraged soil freezing, respectively), and on the thermal insulation and moisture retention of the combined forest floor, moss and lichens layers (ta < jp < bs). Key words: Jack pine, aspen, black spruce, soil moisture, soil temperature, frost penetration, snowpack, boreal conditions
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".