Effects of epilimnetic versus metalimnetic fertilization on the phytoplankton and periphyton of a mountain lake with a deep chlorophyll maxima
Bibliographic record
Abstract
Nutrients can load directly to either the epilimnion or metalimnion of lakes via either differential inflow depths of tributaries or intentional fertilization of discrete strata. We evaluated the differential effects of epilimnetic versus metalimnetic nutrient loading using 17-m-deep mesocosms that extended into the deep chlorophyll layer of oligotrophic Pettit Lake in the Sawtooth Mountains of Idaho. Addition of nitrogen plus phosphorus stimulated primary production nearly identically (2.4- to 4-fold on different dates) in both treatments, with the production peaks occurring in the strata where nutrients were added. The metalimnetic fertilization, however, resulted in equal or greater stimulation of chlorophyll a and phytoplankton biovolume than when nutrients were added directly to the epilimnion. Periphyton growth was stimulated 10100 times more by epilimnetic fertilization than by metalimnetic fertilization and diverted nutrients from the planktonic autotrophs. These results suggest that the development of deep chlorophyll layers may be influenced by plunging river inflows that carry nutrients to the metalimnion and that metalimnetic lake fertilization may be useful as a tool for increasing lake productivity while reducing the impact on water quality.
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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 | 0.000 |
| 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) | 0.001 | 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".