Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With changes in ice cover duration, nutrient loading, and anoxia risk, it is important to understand the mechanisms that control nitrogen cycling and oxygen depletion in lakes through winter. Current understanding is largely limited to description of changes in chemistry, with few measurements of the processes driving winter changes, how they differ across lakes, and how they are impacted by under-ice conditions. Nitrification is a process which consumes oxygen and ammonium (NH4+), and supplies nitrate (NO3-). To date, nitrification has been measured under ice cover in only two lakes globally. Here, we used 15NH4+ enrichment to measure rates of pelagic nitrification in thirteen water bodies in two ecozones. Our work demonstrates ecologically important rates of nitrification can occur despite low water temperatures, impacting NH4+, NO3- and, most importantly, oxygen concentrations. However, high rates are not the norm. When, where and why is nitrification important in winter? We found that nitrification rates were highest in a eutrophic lake chain downstream of a wastewater treatment effluent (mean: 226.5 μg N L-1 d-1), and in a semi-saline prairie lake (110.0 μg N L-1 d-1). In the boreal shield, a eutrophic lake had nitrification rates exceeding those of an oligotrophic lake by 6-fold. Supplementing our results with literature data we found NH4+ concentrations were the strongest predictor of nitrification rates across lentic ecosystems in winter. Higher nitrification rates were associated with higher concentrations of NH4+, NO3- and nitrous oxide (N2O). While more work is required to understand the switch between high and low nitrification rates and strengthen our understanding of winter nitrogen cycling, this work demonstrates that high nitrification rates can occur in winter.
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.
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 | 0.000 | 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) | 0.005 | 0.013 |
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 it