Water transparency and nutrients as controls on phytoplankton along a flood-frequency gradient among lakes of the Mackenzie Delta, western Canadian Arctic
Bibliographic record
Abstract
Relatively low phytoplankton production among lakes of the Mackenzie Delta has been attributed to light limitation in frequently flooded lakes and to nutrient limitation among infrequently flooded lakes; productivity peaks have been attributed to optimal lightnutrient conditions at intermediate flood frequency. We found that the distribution and abundance of phytoplankton among a large number of lakes was explained by optimal lightnutrient conditions but not by nutrient or light limitation. For a subset of lakes, seasonal dynamics of biomass were consistent with light limitation and optimal lightnutrient conditions but not nutrient limitation; photosynthetic rates were consistent with optimal lightnutrient conditions and nutrient limitation but not light limitation. Results of dilutiondeletion experiments across a lightnutrient gradient indicated transition from light limitation to optimal lightnutrient conditions to nutrient limitation. Surprisingly, adding river water to lake water did not increase photosynthetic rates; this result and experimental incubations in situ during river inflow and lake outflow suggested that continuous supply of river water may be necessary to increase phytoplankton growth rates. Among infrequently flooded lakes, phytoplankton response to nutrient additions showed that phosphorus (P) limitation was no more likely than nitrogen (N) limitation, co-limitation, or no limitation by N or P.
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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 | 0.001 |
| 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".