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Record W2158217173 · doi:10.1071/mf12166

Drivers of long-term trends and seasonal changes in total phosphorus loads to a mesotrophic lake in the west of Ireland

2013· article· en· W2158217173 on OpenAlexaff
Eleanor Jennings, Norman Allott, David Lenihan, Bill Quirke, David Taylor, Helena Twomey

Bibliographic record

VenueMarine and Freshwater Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsTrinity College
Fundersnot available
KeywordsEnvironmental scienceEutrophicationPhosphorusSeasonalityWater qualityHydrology (agriculture)HindcastPollutionNutrientEcologyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Excess phosphorus (P) loading is a major cause of deterioration in surface water quality. In Ireland, regulation has focussed on control of P losses from agriculture and wastewater treatment plants (WWTPs): the two main sources of excess P. Hindcast modelling for Lough Leane, south-west Ireland, indicated that, while the only municipal (point) source contributed up to 41% of the annual TP loading until the mid 1980s, over 90% of the TP load was from diffuse sources following upgrading of the WWTP. Field data from 2000–2006 confirmed that most of the TP load came from agriculture, with 73% being exported between September and February, generally the wettest months in the region. However, the WWTP contributed up to 60% of daily loads during summer. Short lake residence times (two to four months) between October and February indicated that external loadings during these months were unlikely to make a significant contribution to summer phytoplankton growth in the lake. In contrast, the potential effects of point sources during low flows were maximised by longer residence times between April and September. The results highlight the importance to aquatic pollution impacts of, and therefore the need for regulatory responses to respect, seasonal variations in loading and residence time.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.999

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)0.0010.000

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 head0.017
GPT teacher head0.263
Teacher spread0.245 · 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.

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

Citations6
Published2013
Admission routes1
Has abstractyes

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