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Record W1528213828 · doi:10.1080/14634988.2012.738997

An integrated approach to identifying ecosystem recovery targets: Application to the Bay of Quinte

2012· article· en· W1528213828 on OpenAlexafffund
E. Agnes Blukacz‐Richards, Marten A. Koops

Bibliographic record

VenueAquatic Ecosystem Health & Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsEnvironment and Climate Change CanadaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaMinistry of Natural Resources
KeywordsDreissenaPhytoplanktonEnvironmental scienceBenthic zoneTrophic levelBiomass (ecology)BiomanipulationBayZooplanktonEcosystemFisheryFood webTrophic cascadePopulationEcologyOceanographyBiologyNutrientBivalvia

Abstract

fetched live from OpenAlex

In 1985, the International Joint Commission identified the Bay of Quinte as an Area of Concern due to its degraded ecosystem. A Remediation Action Plan was established with delisting targets including the goals of decreasing phosphorous loading and restoring the upper (fish and wildlife) and lower (phytoplankton, zooplankton, and benthic invertebrates) trophic levels. We examined the consistency among seven Remedial Action Plan targets using Ecopath, a mass-balance model, for the upper Bay of Quinte for the post Zebra Mussel (Dreissena polymorpha) invasion period (1995–2002). We quantified the trophic consequences of bottom-up-control by gradually reducing the phytoplankton biomass until the Ecopath model became unbalanced (27% reduction). Replicate (n = 25) mass-balance solutions consistently showed that reductions in Zebra Mussel biomass were necessary to achieve mass-balance. This bottom-up control met the nutrient (total phosphorus), fish, benthic invertebrates, and phytoplankton population RAP targets. Based on these consistent results, it is recommended that future modelling efforts examine the effects of further phytoplankton biomass reductions.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.813
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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 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

Citations11
Published2012
Admission routes2
Has abstractyes

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