Long‐Term and Seasonal Drivers of Organic Matter in the Clearwater Tapajós River and Implications for the Amazon River Basin
Bibliographic record
Abstract
Abstract The Amazon River exports over 10% of the global riverine dissolved organic carbon (DOC) flux to the ocean. However, several downstream clearwater tributaries, such as the Tapajós River, are typically not included in these measurements, omitting a crucial part of the Amazon carbon cycle. This study investigated near‐monthly DOC and dissolved organic matter (DOM) composition via optical, fluorescence spectroscopy, and ultra‐high resolution mass spectrometry (FT‐ICR MS) of the Tapajós River for 8 years (2016–2024) to better understand patterns and drivers of potential organic carbon export to the lower Amazon River. DOM composition and DOC export were driven by the seasonal flood pulse of the Tapajós River, exporting aromatic terrestrial DOM from the watershed during high discharge and internally produced algal or microbial DOM during dry periods. On average, we report that the Tapajós River exports 1.38 Tg DOC annually to the downstream Amazon mixing zone, representing an amount of DOC exported by other major world rivers such as the Yukon or Mekong River. Furthermore, organic carbon export varied interannually with less DOC exported during dry El Niño events and more algal‐derived DOM exported during bloom periods. Finally, as grassland and cropland landcover increased over the study period, we observed an average decrease in aromatic DOM and an increase in microbially processed fluorophores. Our study suggests that temperature, precipitation, and anthropogenic land use changes in clearwater rivers will impact carbon export across the lower Amazon River network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".