Variable impact of wildfire smoke on ecosystem metabolic rates in lakes
Bibliographic record
Abstract
Increasingly severe wildfires release smoke plumes that cover entire continents, depositing aerosols and reducing solar radiation fluxes to millions of freshwater ecosystems, yet little is known about their impacts on inland waters. This large scale study 1) quantified annual and seasonal trends in the spatial extent of dense smoke cover in California, USA, over the last 18 years (2006 - 2022), and 2) assessed the impacts of dense smoke cover on daily gross primary production (GPP) and ecosystem respiration (R) in 10 lakes spanning a large gradient in nutrient concentration and water clarity, during the three smokiest years in our dataset (2018, 2020, 2021). We found that the maximum spatial extent of dense smoke cover between June-October has increased to 70% of California’s area since 2006, with the greatest increases in August and September. In the three smokiest years, lakes were exposed to an average of 33 days of dense smoke between July and October, resulting in substantial reductions in shortwave radiation fluxes and 3 to 4-fold increases in atmospheric fine particulate matter concentrations (PM2.5). However, responses of lake GPP to smoke cover were extremely variable among and within lakes, as well as between years. In contrast, the response of rates of ecosystem respiration to smoke was related to lake nutrient concentrations and water temperature –respiration rates decreased during smoke cover in cold, oligotrophic lakes but not in warm, eutrophic lakes. The impacts of dense, prolonged smoke cover on inland waters are likely to be highly variable within and among regions due to mediating effects of lake attributes and seasonal timing of wildfires.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".