Impact of Seasonal Variations and Nutrient Inputs on Nitrogen Cycling and Degradation of Hexadecane by Replicated River Biofilms
Bibliographic record
Abstract
Biofilm communities cultivated in rotating annular bioreactors using water from the South Saskatchewan River were assessed for the effects of seasonal variations and nutrient (C, N, and P) additions. Confocal laser microscopy revealed that while control biofilms were consistently dominated by bacterial biomass, the addition of nutrients shifted biofilms of summer and fall water samples to phototrophic-dominated communities. In nutrient-amended biofilms, similar patterns of nitrification, denitrification, and hexadecane mineralization rates were observed for winter and spring biofilms; fall biofilms had the highest rates of nitrification and hexadecane mineralization, and summer biofilms had the highest rates of denitrification. Very low rates of all measured activities were detected in control biofilms (without nutrient addition) regardless of season. Nutrient addition caused large increases in hexadecane mineralization and denitrification rates but only modest increases, if any, in nitrification rates, depending upon the season. Generally, both alkB and nirK were more readily PCR amplified from nutrient-amended biofilms. Both genes were amplified from all samples except for nirK from the fall control biofilm. It appears that bacterial production in the South Saskatchewan River water is limited by the availability of nutrients and that biofilm activities and composition vary with nutrient availability and time of year.
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 | 0.000 |
| 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 | 0.001 | 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".