Elucidating the influence of freshwater and sympagic inputs on biogeochemical cycling in the Canadian Arctic Archipelago
Bibliographic record
Abstract
The Canadian Arctic Archipelago (CAA) receives significant freshwater inputs from rivers, sea ice, and glacial melt that influence ocean processes and biogeochemical cycles. This study presents a baseline dataset of hydrography and isotopic composition of seawater and suspended particulate organic matter from summer 2019, providing a framework to evaluate long-term biogeochemical changes due to Arctic shifts. Using salinity-δ 18 O relationships, temperature, Chl-a fluorescence, and macronutrients, interactions between ocean, cryosphere, and terrestrial inputs were characterized. δ 18 O indicated glacial and sea ice melt contributions associated with salinity-influenced stratification and depleted surface nutrients. Notably, surface nitrate+nitrite in the freshest waters (0.06-1.2 μM at S=25-29 psu) showed no enrichment compared to saline waters (S>30 psu), with no correlation between nutrients and salinity. Depleted δ 15 N PON (particulate organic nitrogen) and δ 13 C POC (particulate organic carbon) were observed across the region. Low temperatures, pCO 2, and sea ice likely contributed to low δ 13 C POC , although overlapping drivers of PON and POC isotopic signatures remain unclear. δ 13 C POC values became enriched with increasing POC concentration (r 2 =0.45 surface, r 2 =0.61 at Chl-a maximum), with the most depleted values (-34‰) at the lowest POC. Enriched δ 13 C POC and δ 15 N PON correlated with elevated carbon (C) biomass in channels with sea ice, possibly linked to sympagic algal inputs. Nitrogen limitation occurred across transects, with N:P (NO 3 :PO 4 ) ratios consistently below Redfield (16:1), unalleviated by freshwater inputs. These observations serve as a reference for future studies on freshwater impacts on Arctic productivity, with July-August sampling reflecting post-bloom conditions where freshwater primarily acts as a barrier to mixing, not a nutrient source.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".