Growth as a function of sea ice cover, light and temperature in the arctic/subarctic coralline C. compactum: A year-long in situ experiment in the high arctic
Bibliographic record
Abstract
Long-term, high-resolution measurements of environmental variability are sparse in the High Arctic. In the absence of such data, we turn to proxies recorded in the layered skeletons of the long-lived crustose coralline algae Clathromorphum compactum. Annual growth banding in this alga is dependent on several factors that include temperature, light availability, nutrients, salinity, and calcium carbonate saturation state. It has been observed that growth slows during winter as sunlight reaching the seafloor diminishes due to decreased insolation and the build-up of sea-ice, such that the relationship between sea-ice cover extent and algal growth has allowed for reconstructions of relative sea-ice variability through time. However, recent laboratory work has shown that C. compactum continue growing in complete darkness (sea-ice cover). Therefore, a more complete understanding of algal growth is necessary for the refinement of the sea-ice proxy. Here, we present the results of a ~year-long in-situ growth and environmental monitoring experiment in Arctic Bay, Nunavut, Canada (~73°N) which addresses, for the first time in situ, the gaps in our understanding of growth over an annual cycle in the High Arctic. Algal growth was assessed on a quasi-monthly basis, where specimens were subsampled to quantify monthly extension in the context of ocean temperature and light availability. By measuring extension rate through time, we observed that the algae grew on average 72 µm yr-1, with ~54% of annual growth occurring during the sea-ice free summer months (June-September), ~25% during the winter months (November-April), and ~21% occurring during the transition months of May and October. Although winter growth slowed, we did not observe a consistent cessation of linear extension during low-or no-light months. We posit that substantial growth during the winter months at this latitude is most likely a consequence of the mobilization of stored energy (photosynthate) produced during the photosynthetically active summer months. However, we also discuss the possibility of low light-photosynthetic activity and/or dark carbon fixation, which could also facilitate extension through time. Overall, the novel growth model presented here has implications for the use of C. compactum growth for reconstructing the environment as well as for trace-element-based (typically Mg/Ca) algal chronologies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".