Bubble Distribution Along Major Rivers in the Amazon During the High‐Water Season
Bibliographic record
Abstract
Abstract Ebullition (gas bubbling) from aquatic ecosystems is an important pathway for gas transport from waterbodies to the atmosphere. The spatial and temporal variability of bubbles is typically high and often driven by the distribution of temporary or permanent hotspots. Understanding ebullition patterns remains key to adequately quantifying the exchange of poorly soluble gases like methane. In this study, we performed a spatial analysis of bubble quantity and relative size using a scientific echosounder during the high‐water season across black‐, white‐, and clearwater river floodplains, encompassing a wide variety of waterbody types such as lakes, main fluvial channels, tributaries, and flooded forests in the Amazon River basin. Our results revealed the dominance of a few spatially limited hotspots. While the number of bubbles per m 2 was highest in the Negro river system, the total bubble flux was dominated by a few measurement sections in the Amazon and Tapajós river systems with substantially higher ebullition. The relationships of river system, waterbody type, or depth with the bubble quantity or size were weak with no clear trends. These findings highlight ebullition hotspots as an important component of regional gas emissions in the Amazon basin, emphasizing the spatial heterogeneity of ebullition and the importance of local conditions in regulating ebullition and associated gas fluxes.
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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 | 0.001 | 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".