Data for: 'Applicability of the steady-state oxygen stable isotope method for estimating metabolism in low-productivity Arctic lakes'
Bibliographic record
Abstract
The data (curated input data and results) used in the manuscript: "Applicability of the steady-state oxygen stable isotope method for estimating metabolism in low-productivity Arctic lakes" Each .zip file is a folder containing associated data and explanatory readme files (.txt) Abstract: Metabolism is a key property of lake ecosystem functioning, but logistical challenges make it difficult to estimate across remote regions. The steady-state dissolved oxygen (DO) stable isotope method (18O method) estimates metabolism from discrete water samples and thus enables large-scale surveys. However, this method relies on the assumptions that the upper mixed layer DO saturation (DO%) relative to its isotopic composition (δ18ODO) is at a steady state and that an increase in DO% results in a proportional decrease in δ18ODO. The applicability of these assumptions has not been broadly assessed for small, low-productivity lakes with predominantly benthic metabolism. We evaluated the 18O method in these types of systems by surveying 184 Arctic lakes in Sweden and found that the method consistently produces realistic estimates of metabolism in well-mixed conditions and when water temperatures were relatively stable. Under such conditions, results from the 18O method agreed with those from the free-water diel DO method and rates derived from both methods responded similarly to environmental drivers. In contrast, we found that the 18O method frequently generated unrealistic metabolic rates when temperatures were rising. Increasing temperatures may increase DO% irrespective of δ18ODO in the upper mixed layer and promote lake stratification, both violating the assumptions of the 18O method and preventing benthic metabolism from being integrated by surface water samples. We conclude that the 18O method is a powerful tool for studying metabolism in Arctic lakes across large spatial gradients, provided that temperature dynamics and vertical stratification are considered.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.468 | 0.301 |
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".