Riverine Influence on Coastal Biogeochemistry Along a 400‐km Latitudinal Gradient in Eastern James Bay
Bibliographic record
Abstract
Abstract Rivers integrate climate signals, landscape gradients, and environmental disturbances at the watershed scale, strongly influencing downstream ecosystems and ultimately coastal waters. Watershed environmental gradients therefore exert a strong local influence on coastal river plumes, yet it is unclear how regional‐scale gradients involving multiple watersheds are coupled to broad patterns of coastal marine biogeochemistry and productivity. Here, we aimed to establish connections between the physicochemical properties of rivers draining into the highly riverine‐influenced James Bay (JB) and of the properties of coastal waters along its entire eastern shore. We clustered 17 river outlets and over 140 coastal sites along a 400‐km latitudinal gradient of the eastern JB, sampled during two consecutive summers, according to trends in nutrients, suspended particulate matter, colored dissolved organic matter, freshwater discharge, and salinity. Our findings reveal notable latitudinal changes in the physicochemical properties of both rivers and coastal waters along JB, which were spatially coherent. Whereas river discharge exerts a significant influence—higher discharge amplifies the riverine impact on coastal waters—this alone does not account for the observed variability along the coast. The riverine influence differs among areas and depends on variables considered. In this study, we identified biogeochemical transition zones and assessed the impact of river exports on coastal waters along JB, and this integrative approach could be applied to disentangle river‐coast interactions in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".