Regulated vs. unregulated rivers: Impacts on CDOM dynamics in the eastern James Bay
Bibliographic record
Abstract
The eastern James Bay (EJB) coast harbors numerous rivers, but there is a dearth of knowledge concerning dissolved organic matter (DOM) in the downstream coastal water. Here we report a four-year (2018–2021) and multi-seasons field study on the mixing behavior and characteristics of the chromophoric DOM (CDOM) in the nearshore EJB. Freshwater discharged from the extensively regulated La Grande River (LGR) was constantly depleted in DOM compared with the unregulated rivers (URRs), being on average 3.35 times lower in CDOM absorption coefficient at 440 nm (aCDOM(440)) and 2.50 times lower in dissolved organic carbon (DOC). In contrast, the absorption spectral slope between 275 and 295 nm (S275–295, a proxy of molecular weight) and the specific absorption coefficient at 254 nm (a*CDOM(254), an indicator of aromaticity) of the LGR CDOM were only 10.6% lower and 11.7% higher than those of the URRs CDOM, respectively. Riverine input was found to be the dominant source of CDOM in the study area, with little influence from sea ice formation or melting. CDOM distribution fell into two distinct regimes: the LGR-influenced low-CDOM area in the north and the URRs-influenced high-CDOM area in the south. The two areas showed strong conservative but separate aCDOM(440)–salinity relationships converging at a common marine endmember (salinity ~25) with little seasonality. The composite data combing both areas and all seasons and years exhibited non-linear relationships between S275–295, a*CDOM(254) and aCDOM(440) and a robust simple linear correlation of DOC to aCDOM(440). This study suggests a strong impact of river regulation on CDOM input into the EJB, reveals low seasonal variability of CDOM mixing dynamics and characteristics, and demonstrates the feasibility of using remote sensing from space for real-time and synoptical assessment of DOM dynamics and the associated biogeochemical cycles in the EJB.
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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 | 0.001 |
| 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) | 0.002 | 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".