Oxygen fluxes beneath Arctic land-fast ice and pack ice: towards estimates of ice productivity
Bibliographic record
Abstract
Sea-ice ecosystems are among the most extensive of Earth’s habitats; yet its autotrophic and heterotrophic activities remain poorly constrained. We employed the in situ aquatic eddy-covariance (AEC) O 2 flux method and laboratory incubation techniques (H 14 CO 3 − , [ 3 H] thymidine and [ 3 H] leucine) to assess productivity in Arctic sea-ice using different methods, in conditions ranging from land-fast ice during winter, to pack ice within the central Arctic Ocean during summer. Laboratory tracer measurements resolved rates of bacterial C demand of 0.003–0.166 mmol C m −2 day −1 and primary productivity rates of 0.008–0.125 mmol C m −2 day −1 for the different ice floes. Pack ice in the central Arctic Ocean was overall net autotrophic (0.002–0.063 mmol C m −2 day −1 ), whereas winter land-fast ice was net heterotrophic (− 0.155 mmol C m −2 day −1 ). AEC measurements resolved an uptake of O 2 by the bottom-ice environment, from ~ − 2 mmol O 2 m −2 day −1 under winter land-fast ice to~ − 6 mmol O 2 m −2 day −1 under summer pack ice. Flux of O 2 -deplete meltwater and changes in water flow velocity masked potential biological-mediated activity. AEC estimates of primary productivity were only possible at one study location. Here, productivity rates of 1.3 ± 0.9 mmol O 2 m −2 day −1 , much larger than concurrent laboratory tracer estimates (0.03 mmol C m −2 day −1 ), indicate that ice algal production and its importance within the marine Arctic could be underestimated using traditional approaches. Given careful flux interpretation and with further development, the AEC technique represents a promising new tool for assessing oxygen dynamics and sea-ice productivity in ice-covered 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 | 0.000 |
| Science and technology studies | 0.000 | 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".