Bacterioplankton production, abundance, and nutrient limitation among lakes of the Mackenzie Delta (western Canadian arctic)
Bibliographic record
Abstract
The effects of nutrient availability and quality of dissolved organic carbon (DOC) on bacterioplankton production were assessed in six lakes with differing frequencies of river flooding. Bacterial productivity, dissolved nutrients, and DOC were tracked weekly throughout the open-water period of 2001. Inorganic nutrient (N and P) enrichment microcosm experiments were conducted to directly assess the effects of DOC quality (i.e., mixtures of colored and noncolored DOC) and inorganic nutrient limitation on bacterial productivity among the lakes. Averaged over the open-water season, both abundance and production of bacterioplankton increased with decreasing flood frequency (R2 = 0.61 and R2 = 0.78, respectively). Reduced bacterial production occurred in frequently flooded lakes, where colored DOC, light attenuation, and phosphate were high but ammonium was low. Bacterial production was greatest in infrequently flooded lakes, where noncolored DOC and ammonium were high but phosphate was low. Bacterial production was enhanced by amendments of inorganic nutrients in duplicate experiments (two-factor analyses of variance). Production was also enhanced in response to higher concentrations of either colored or noncolored DOC following release from inorganic nutrient limitation. Size fractionated (<1 µm versus >1 µm) N-debt and P-debt bioassays typically showed demand for P and release of N by bacteria in all study lakes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".