FTIR autecological analysis of bottom-ice diatom taxa across a tidal strait in the Canadian Arctic
Bibliographic record
Abstract
A recent study demonstrated that an Arctic tidal strait, where a shoaled and constricted waterway increases tidally driven sub-ice currents and turbulence, represents a “hotspot” for ice algal production due to a hypothesized enhanced ocean-ice nutrient supply. Based on these findings, we sampled the bottom-ice algal community across the same tidal strait between the Finlayson Islands within Dease Strait, Nunavut, Canada, in spring 2017. Our objective was to examine cellular responses of sea-ice diatoms to two expected nutrient supply gradients in their natural environment: (1) a horizontal gradient across the tidal strait and (2) a vertical gradient in the bottom-ice matrix. Two diatom taxa, Nitzschia frigida and Attheya spp. in bottom-ice sections (0–2, 2–5, and 5–10 cm) under thin snow cover (<5 cm), were selected for Fourier Transform Infrared (FTIR) spectrochemical analysis for lipid and protein content. Results from the FTIR technique strongly supported the existence of a horizontal nutrient gradient across the tidal strait of the Finlayson Islands, while estimates of particulate organic carbon and chlorophyll a concentrations were difficult to interpret. The larger N. frigida cells appeared to be more sensitive to the suspected horizontal nutrient gradient, significantly increasing in lipid content relative to protein beyond the tidal strait. In contrast, the epiphytic diatoms, Attheya spp., were more sensitive to the vertical gradient: above 2 cm in the bottom-ice matrix, the non-motile cells appeared to be trapped with a depleted nutrient inventory and evidence of a post-bloom state. Application of the FTIR technique to estimate biomolecular composition of algal cells provided new insights on the response of the bottom-ice algal community to the examined spatial gradients that could not be obtained from conventional bulk measurements alone. Future studies of sea ice and associated environments are thus encouraged to employ this technique.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".