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Record W2155767384 · doi:10.1111/are.12881

Monitoring and modelling total phosphorus contributions to a freshwater lake with cage-aquaculture

2015· article· en· W2155767384 on OpenAlexaffabout
Jacquiline E Milne, Christopher H. Marvin, Ram Yerubandi, Kevin S. McCann, Richard D. Moccia

Bibliographic record

VenueAquaculture Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Guelph
Fundersnot available
KeywordsHydrology (agriculture)AquacultureWater qualityEnvironmental scienceInletEutrophicationLitterGroundwaterPhosphorusCageEnvironmental engineeringEcologyBiologyFisheryNutrientOceanographyFish <Actinopterygii>ChemistryMathematics

Abstract

fetched live from OpenAlex

A mass-balance modelling approach combined with a sensitivity analysis was utilized to gain an improved understanding of the relative contributions of phosphorus (P) loading from various anthropogenic and non-anthropogenic sources into Lake Wolsey (Manitoulin Island, Ontario, Canada), a Type 2 freshwater lake with a cage-aquaculture facility. Total P loadings were estimated from eight sources (inlet exchange, non-point sources, cage-aquaculture facility, internal loading, groundwater seepage, atmospheric deposition, leaf litter and dwellings) and three sinks (outlet exchange, sedimentation and sportfishing). Results indicated that over the study period (May–November 2007) the non-point sources were the leading contributor of total P to Lake Wolsey (1120 kg P) followed by the cage-aquaculture facility loading (915 kg P), inlet exchange (539 kg P), groundwater inputs (305 kg P), dwellings (219 kg P), internal P recycling loads from the hypoxic hypolimnion (186 kg P), atmospheric deposition (79 kg P) and decomposing leaf litter (8.1 kg P). When comparing the loadings in this study, the sensitivity analysis showed that non-point sources were the only significant input parameter of total P loading to the in-lake concentrations of P in Lake Wolsey(P = 0.05). Information from this project will provide water quality managers with sound scientific information to make defencible decisions pertaining to policy and regulatory approaches for water quality risk assessment and management of cage-aquaculture in Type 2 sites.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.041
GPT teacher head0.316
Teacher spread0.275 · 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 designNot applicable
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

Citations5
Published2015
Admission routes2
Has abstractyes

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