Modelling and predicting ecosystem exposure to in-feed drugs discharged from marine fish farm operations : an Initial perspective
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".