Dissolved oxygen dynamics under ice: Three winters of high‐frequency data from <scp>L</scp>ake <scp>T</scp>ovel, <scp>I</scp>taly
Bibliographic record
Abstract
Abstract Under‐ice dissolved oxygen (DO) metabolism and DO depletion are poorly understood, limiting our ability to predict how changing winter conditions will affect lake ecosystems. We analyzed under‐ice DO dynamics based on high‐frequency (HF) data at two depths (5 and 25 m) for three winters (January–March 2014, 2015, and 2016) in oligotrophic Lake Tovel (1178 m above sea level; maximum depth 39 m). Specifically, we assessed diel metabolic rates based on HF data of DO, temperature, and light for winter 2016 and seasonal DO depletion rates based on HF data of DO for all three winters. For 2016, calculations of metabolic rates were possible only for 34% and 3% of days at 5 and 25 m, respectively; these metabolic rates generally indicated net heterotrophy at both depths. Low success in modeling metabolic rates was attributed to low diel DO variability and anomalous diel DO patterns, probably linked to under‐ice physical processes. Seasonal DO patterns for the three winters showed increasing, decreasing, or stable DO trends at 5 m while at 25 m patterns always showed decreasing DO trends but with different rates. Our multiyear study permitted us to hypothesize that the observed intraannual and interannual differences in DO depletion can be attributed to variable snow cover determining the penetration of radiation and thus photosynthesis. This study brings new insights to DO dynamics in ice‐covered systems, highlights the challenges linked to under‐ice lake metabolism, and advocates for a modeling approach that includes physical processes.
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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 | 0.000 | 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) | 0.000 | 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".