Variability in lake bacterial growth and primary production under lake ice: Evidence from early winter to spring melt
Bibliographic record
Abstract
Abstract Climate change is causing seasonally ice‐covered lakes of the boreal region to undergo changes in their winter regime by altering patterns of precipitation and temperature, often reflected as reduced snow and ice cover duration. The duration, extent and quality of ice, and snow cover have a pivotal role for production and carbon cycling in lakes in winter, with potentially cascading effects for the following open water period. We investigated under‐ice carbon cycling by assessing bacterial growth (including bacterial production, bacterial respiration, and bacterial growth efficiency) and primary production at five water depths during early winter, midwinter, late winter and melting season in a boreal lake, and report significantly different temporal patterns. Bacterial respiration was dominant in early and midwinter, whereas the late winter and melting season were dominated by bacterial production. Multiple linear regression models indicated that high early winter bacterial respiration was associated with senescing phytoplankton, whereas bacterial production was promoted by the onset of spring processes. Collectively, bacterial growth indices were inherently linked with bacterioplankton community composition and specific biomarker taxa. Primary production under ice increased in late winter when light‐blocking snow cover melted, and primary production measured from the lake ice exceeded that of the water column at the melting season. Ice samples hosted diverse eukaryotic communities including photoautotrophs, suggesting that the habitat potential of the understudied lake ice and the role of ice for ecological processes at ice melt should be further explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".