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Record W2032554059 · doi:10.1139/f09-003

Biogenic calcite–phosphorus precipitation as a negative feedback to lake eutrophication

2009· article· en· W2032554059 on OpenAlexvenueno aff
Stephen K. Hamilton, Denise A. Bruesewitz, Geoffrey P. Horst, David B. Weed, Orlando Sarnelle

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCalciteEutrophicationAlkalinityCarbonateCalcium carbonateSink (geography)Environmental chemistryPrecipitationPhosphorusCarbon dioxideTotal inorganic carbonSedimentNutrientEnvironmental scienceChemistryGeologyMineralogy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.220
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations87
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicAquatic Ecosystems and Phytoplankton DynamicsFrench-language works237,207