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Record W4387602335 · doi:10.3897/aca.6.e107183

Phosphorus and Lake Eutrophication: Recent Findings and Emerging Challenges

2023· article· en· W4387602335 on OpenAlexafffund
Philippe Van Cappellen

Bibliographic record

VenueARPHA Conference Abstracts · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Waterloo
FundersGlobal Water FuturesCanada First Research Excellence Fund
KeywordsEutrophicationEnvironmental scienceAlgal bloomPhosphorusStormwaterAquatic ecosystemNutrientSurface runoffHydrology (agriculture)UrbanizationWater resource managementEnvironmental protectionEcologyPhytoplankton

Abstract

fetched live from OpenAlex

This presentation reviews some recent and ongoing research on phosphorus (P) cycling in a variety of lake systems. Phosphorus is an essential nutrient element, and its anthropogenic enrichment is generally considered to be the main driver of cultural eutrophication of freshwater lakes, which, in the worst case, leads to the occurrence of harmful algal blooms, the intensification of hypoxia and the die-off of aquatic life. The research presented shows that excess external P loading causes the accumulation of reactive chemical P forms in the bottom sediments of lakes (Update #1). The slow release of this reactive legacy P back to the water column can significantly delay a lake’s recovery following the reduction of external P loading (Update #2). Land use changes accompanying agricultural intensification and urbanization generally increase P emissions, but the implementation of agricultural and stormwater best management practices can effectively mitigate external P loads to receiving lakes (Update #3). However, additional stressors, including climate change and salinization, magnify in-lake P mobilization pathways and, hence, increase the risks of lake (re-)eutrophication (Update #4).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.504

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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

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