Bibliographic record
Abstract
Abstract. Area burned, number of fires, seasonal fire severity, and fire season length are all expected to increase in Canada, with largely unquantified ecosystem feedbacks. However, there are few observational studies measuring ecosystem-scale biogeochemical (e.g., carbon dioxide exchanges) and biophysical (e.g., energy partitioning) properties during smoke episodes and hence assessing responses of gross primary production (GPP) to changes in incoming diffuse photosynthetically active radiation (PAR). In this study, we leveraged two long-term eddy covariance measurement sites in forest and wetland ecosystems to study four smoke episodes, which happened at different times and differed in length, over 4 different years (2015, 2017, 2018, and 2020). We found that the highest decrease in shortwave irradiance due to smoke was about 50 % in July and August but increased to about 90 % when the smoke arrived in September. When the smoke arrived in the later stage of summer, impacts on sensible and latent heat fluxes were very different than the earlier ones. Smoke generally increased the diffuse fraction (DF) from ∼ 0.30 to ∼ 0.50 and turned both sites into stronger carbon dioxide (CO2) sinks with increased GPP up to ∼ 18 % and ∼ 7 % at the forest and wetland site, respectively. However, when DF exceeded 0.80 as a result of dense smoke, both ecosystems became net CO2 sources as total PAR dropped to low values. The results suggest that this kind of natural experiment is important for validating future predictions of smoke–productivity feedbacks.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.326 | 0.233 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".