Freeze/Thaw cycle monitoring using multi-scale SMAP products and hydrothermal modeling over the Canadian tundra: Final Research Report 2015-2019.
Bibliographic record
Abstract
The seasonal Freeze/Thaw (F/T) cycle is a major phenomenon in the climate system and plays an important role in ecosystem functioning by influencing the rate of photosynthesis and respiration of the vegetation, reducing evaporation, reducing the penetration of water into the soil and altering surface runoff. Boreal and arctic regions form a complex land cover mosaic where vegetation structure, condition and distribution are strongly regulated by environmental factors such as soil moisture and nutrient availability, permafrost, growing season length and disturbance. In these seasonally frozen environments, the growing season is determined primarily by the length of the non-frozen period. Variations in both the timing of spring thaw and the resulting growing season length have been found to have a major impact on terrestrial carbon exchange and atmospheric CO2 source/sink strength in boreal regions. The frozen soil mapping can be improved by using the NASA SMAP instrument which has a Radiometer at L-band (1.20-1.41 GHz). In fact, SMAP is able to monitor the frozen soil because of its ability to sense the soil conditions through moderate land cover. The accuracy, resolution, and global coverage of the SMAP mission make possible a systematic updating of frozen ground maps and monitoring the seasonal F/T cycle. The main purpose of this project was to enhance the Canadian Plan for SMAP related F/T products by 1) Supporting a ground network in Northern Quebec as a Cal/Val site related to F/T products in Canada; by 2) Testing and validating the SMAP data to monitor F/T state over the Tundra and the Boreal Forest in Canada; and by 3) Developing a hydrothermal model to provide soil moisture and freezing/thawing information in high spatial and temporal resolution at a watershed level. The information is crucial to better understand small scale heterogeneities of F/T related landscape features and to close the scale gap between field monitoring data and SMAP F/T products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".