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Record W7133285097

Modelling and predicting ecosystem exposure to in-feed drugs discharged from marine fish farm operations : an Initial perspective

2023· other· en· W7133285097 on OpenAlexaboutno aff
F. H. Page, M. P. A. O'Flaherty-Sproul, S. P. Haigh, B. D. Chang, D. K. H. Wong, M. J. Beattie

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Deposition (geology)Benthic zoneAquatic ecosystemEcosystemHydrographyConceptual modelAquatic environment
DOInot available

Abstract

fetched live from OpenAlex

This document focuses on modelling in relation to the discharge of active ingredients associated with in-feed drugs used in marine net-pen aquaculture farming operations in Canada. The document includes an overview of the context and associated conceptual processes to be modelled, specific modelling challenges, a review of modelling efforts to date, and a description of some simple models for potential use. In general, modelling for in-feed drug discharges and depositions is in an early stage of development. Few models have been developed specifically to predict the benthic deposition of in-feed drugs and these range in complexity. Model results are sensitive to input parameters, including treatment details, hydrographic conditions, drug partitioning specifics, and sinking rates and timing of discharges. Many of these parameters are poorly understood, difficult to measure, and hence, not well quantified. Thus, determining the quantification of uncertainties and sensitivities of model results remain challenging. Many models for predicting the deposition of organic waste produced by net-pen fish farms have been developed. Although similarities exist between the underlying assumptions of these models and those for in-feed drugs, their adaptation to use for modelling the deposition of in feed drugs is not necessarily simple or straightforward. Of particular importance is the inclusion of drug partitioning specifics which is necessary in order to correctly model in-feed drug deposition dynamics; simple conversion factors between organic waste deposition and in-feed drug deposition are likely not a suitable approach as the ratio between carbon and drug in the released feces varies with time. The objectives of modelling must be specified before a model is selected and assessed for its adequacy and sufficiency. Once models have been selected and/or developed, models must be validated before being used. In general, existing models of in-feed drug deposition have not been extensively calibrated or validated. Of the few validations that have been done, the literature suggests that, regardless of complexity, existing models give, at best, an order of magnitude estimate of seabed drug concentrations. Despite the uncertainties surrounding model precision and validity, models can be useful for regulatory decision support. Model selection depends on the decision maker’s objectives. Precision and accuracy of the model cannot be estimated until the chosen model is verified and validated. Existing validation studies indicate that available models are only able to provide order of magnitude estimates of in-feed drug depositions. At this time, simple models may be sufficient for decision support. This sentiment may change as science better characterizes model inputs and processes, and conducts more validation studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207