High-resolution Canada domain Forest disturbance forcings suitable for land surface modeling applications
Bibliographic record
Abstract
High-spatial resolution, spatially explicit forest fire, and harvest data are useful for prescribing disturbances in land surface models (LSMs) to simulate the impacts of these disturbances on terrestrial processes such as the carbon cycle. Numerous fire and harvest datasets are available within the Canada domain for the period from 1985 - present; however, they vary in their spatial and temporal extent, quality, and format. Before the more modern satellite era, initiated in 1984 with the launch of 30-m spatial resolution Landsat-4), data are more sparse. In this study, we set out to create a spatially explicit forest fire and harvest area disturbance dataset for Canada for the period 1740 - 2018 suitable for use with LSMs. To do so, we cataloged and harmonized disparate spatial and aspatial datasets of historical disturbance in Canada. We then developed a novel algorithm that combines spatial and aspatial disturbance data using stand age to infill sparse forest disturbance data far back in time. Based on different historical scenarios, we place 283-394 Mha of fire disturbance and 3.42 Mha of harvest across the Canadian forest landscape between 1740 and 1918. Once spatial records are available in 1918, we supplement the existing spatial records with 25.79-60.30 Mha of fire and 24.75 Mha of harvest, placing zero disturbance once satellite mapped products become available in 1985. The algorithm's results were verified by examining Canada-wide fire and harvest in the datasets that are fed to the algorithm, and in the LSM drivers that result. Moreover, the diagnostic metrics were examined to detail the relative contribution of the spatial, aspatial, and stand-age data to the algorithm outputs. Our forcings and algorithm will facilitate improvements to the representation of disturbance-mediated impacts on terrestrial carbon cycling in Canada and potentially other regions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".