Rubisco in high Arctic tidewater glacier‐marine systems: A new window into phytoplankton dynamics
Bibliographic record
Abstract
Abstract The hundreds of tidewater glaciers found in the Canadian Arctic Archipelago have the potential to enhance delivery of nutrients and other material to the surface ocean. Despite this, their influence on marine ecosystems, specifically phytoplankton, is poorly characterized. Here we developed and applied a quantitative mass spectrometry‐based approach to measure phytoplankton ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco) concentrations to examine differences in productivity in glacierized and non‐glacierized marine systems in Jones Sound, Nunavut, within Inuit Nunangat. Comparisons to chloroplast 16S rRNA gene amplicon sequencing data suggested that these measurements detect the majority of Rubisco produced in Jones Sound. Because Rubisco catalyzes carbon fixation, we used these measurements to estimate total and group‐specific primary production potential, which were within the range of historical primary production measurements made using classical methods in this region. Our measurements also revealed that up to 2% of total protein in the water column is Rubisco, and that Rubisco concentrations are correlated with chlorophyll fluorescence, with maxima near the nitracline. Rubisco produced by diatom genera Chaetoceros and Thalassiosira were higher in marine regions influenced by glaciers, while Rubisco from Micromonas (Chlorophyta) was greater in non‐glacierized regions. This suggests that future climate scenarios may favor smaller phytoplankton groups, like Micromonas, with consequences for food webs and carbon cycling. This study broadens our understanding of how tidewater glaciers will impact phytoplankton communities, now and in a warmer future, and lays the foundation for using this mass spectrometry‐based approach to quantify phytoplankton group‐specific carbon fixation potential in other marine 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".