Dominance of net autotrophy in arid landscape low relief polar lakes, Nunavut, Canada
Bibliographic record
Abstract
The Arctic is the fastest warming biome on the planet, and environmental changes are having striking effects on freshwater ecosystems that may impact the regional carbon cycle. The metabolic state of Arctic lakes is often considered net heterotrophic, due to an assumed supply of allochthonous organic matter that supports ecosystem respiration and carbon mineralization in excess of rates of primary production. However, lake metabolic patterns vary according to regional climatic characteristics, hydrological connectivity, organic matter sources and intrinsic lake properties, and the metabolism of most Arctic lakes is unknown. We sampled 35 waterbodies along a connectivity gradient from headwater to downstream lakes, on southern Victoria Island, Nunavut, in an area characterized by low precipitation, organic-poor soils, and high evaporation rates. We evaluated whether lakes were net autotrophic or heterotrophic during the open water period using an oxygen isotopic mass balance approach. Most of the waterbodies were autotrophic and sites of net organic matter production or close to metabolic equilibrium. Autotrophy was associated with higher benthic primary production, as compared to its pelagic counterpart, due to the high irradiance reaching the bottom and efficient internal carbon and nutrient cycling. Highly connected midstream and downstream lakes showed efficient organic matter cycling, as evidenced by the strong coupling between gross primary production (GPP) and ecosystem respiration, while decoupling was observed in some headwater lakes with significantly higher GPP. The shallow nature of lakes in the flat, arid region of southern Victoria Island supports net autotrophy in most lakes during the open water season. Ongoing climate changes that lengthen the ice-free irradiance period and increase rates of nutrient evapoconcentration may further promote net autotrophy, with uncertain long-term effects for lake functioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".