Hidden Stores of Organic Matter in Northern Lake Ice: Selective Retention of Terrestrial Particles, Phytoplankton and Labile Carbon
Bibliographic record
Abstract
Abstract Around 50% of the world's lakes freeze seasonally, but the duration of ice‐cover is shortening each year and this is likely to have broad limnological consequences. We sampled freshwater ice and the underlying water in 19 boreal and polar lakes to evaluate whether lake ice contains an inoculum of algae, nutrients, and carbon that may contribute to lake ecosystem productivity. Boreal and Arctic lakes differed in ice duration (6 vs. >10 months), thickness (70 vs. 190 cm), and quality (predominantly snow ice vs. black ice), but in all lakes, there were consistent differences in biological and biogeochemical composition between ice and water. Particulate fractions were often more retained while most dissolved compounds were excluded from the ice; for example, the ice had more terrestrial particulate carbon, measured as fatty acid biomarkers (averages of 1.1 vs. 0.3 µg L −1 ) but lower dissolved organic carbon (2.2 vs. 5.7 mg C L −1 ) and inorganic phosphorus concentrations (4.0 vs. 7.5 µg C L −1 ) than the underlying water. The boreal ice further had three times higher chlorophyll‐ a, than the water (0.9 vs. 0.3 µg L −1 ). Of the dissolved fractions, the contribution of protein‐like compounds was higher in the ice, and this in all lakes. These labile compounds would become available to planktonic microbes when the ice melts. Our results show that freshwater ice has an underestimated role in storage and transformation in the biogeochemical carbon cycle of ice‐covered lake ecosystems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".