Spatial Variability of POC in Surface Waters of the St. Lawrence Estuary and Gulf: A Molecular and Bulk Analysis
Bibliographic record
Abstract
Suspended particulate matter (SPM) in aquatic systems is comprised of many inorganic and organic components with much of the organic matter not characterized. This study characterized and investigated the spatial variability of particulate organic carbon (POC) in the surface waters of the St. Lawrence Estuary and Gulf to discern the contributions of terrestrial and marine organic matter (OM). Using bulk elemental and isotopic analyses alongside lipid molecular biomarkers, specifically hydrocarbons and fatty acids, we characterized POC from surface water samples collected at 19 stations spanning eight scientific missions between 2003 and 2023. This comprehensive dataset is the first to incorporate both molecular and bulk analyses of POC in the surface waters of this system providing a baseline understanding of OM composition and source contributions in this dynamic system. Our findings reveal distinct spatial patterns in OM composition, with terrestrially derived OM dominating the Upper St. Lawrence Estuary (ULSE) and a gradual shift towards marine-derived OM as distance from Quebec City increases i.e., downriver. High molecular weight (HMW) n-alkanes (C27, C29, and C31) are prevalent in terrestrially influenced stations while low molecular weight (LMW) n-alkanes (C15, C17, and C19) dominate marine stations. Stable carbon isotopes (δ13C) and C/N ratios also reflect the transition from depleted to more enriched δ13C values along the gradient. A multivariate approach was used to identify spatial variability using principal component analysis (PCA), broken stick analysis, and SIMPER. Using these techniques, we attempted to identify the primary drivers of OM composition across the continuum. Salinity, n-alkane proxies, and distance from Quebec emerged as key factors influencing OM distribution. Our results suggest that OM composition in the SLEG (St. Lawrence Estuary and Gulf) is controlled by both hydrodynamic processes and terrestrial-marine interactions. This study establishes a critical baseline for understanding the sources and spatial variability of OM in the SLEG which contribute valuable information into the biogeochemical processes that shape this important system. These findings emphasize the need for continued monitoring to evaluate future changes driven by natural and anthropogenic changes as well as climate change.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".