Biogenic calcite–phosphorus precipitation as a negative feedback to lake eutrophication
Bibliographic record
Abstract
Lakes in carbonate-rich watersheds commonly precipitate calcium carbonate as calcite, and this is accelerated by photosynthetic uptake of carbon dioxide. Co-precipitation of phosphate with calcite is one reason that algal growth in such lakes tends to be strongly phosphorus (P) limited. The extent to which calcite precipitation can be a sink for added P, and thus act as a potential negative feedback to eutrophication, was demonstrated using large enclosures within a Michigan lake. Nitrogen and P were added over 45 days in the summer to produce mesotrophic and eutrophic conditions. Algal biomass and production increased markedly with the nutrient additions, elevating the pH (9–10) and greatly increasing calcite precipitation, reducing concentrations of calcium and alkalinity by up to 60%. Sediment traps indicated that calcite sedimentation was a major sink for added P. By the end of the experiment, only about half of the added P remained in the water column. Major ion concentrations in a larger set of Michigan lakes showed calcite precipitation to be widespread, reflecting the abundant carbonate minerals in this glacial region.
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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 | 0.001 |
| 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".