Estimating aquatic phosphorus concentrations 30 metres down-current from a rainbow trout cage array
Bibliographic record
Abstract
Aquatic total phosphorus (Tot-P) is measured 30 m distant from rainbow trout (Oncorhynchus mykiss) cages, for regulatory compliance on the Canadian side of Lake Huron. The ability to predict changes in near-field Tot-P concentrations from changes in production, or to explain near-field concentrations in the event of compromised reference sites, would be very useful for managers and farmers. A logical first step in the development of a near-field model is the estimation of down-current Tot-P concentrations. A nutritional mass balance approach was used to calculate the amount of non-settleable Tot-P which was divided by the corresponding flushing volume over four separate production periods. Despite a number of uncertainties, such as cage effects on flushing volume, the contribution of particulate phosphorus, fish distribution, feed conversion ratio, and ingestion-excretion time; accuracy (slope = 1.06) and precision (r(2) = 0.75) of modelled estimates fit well with empirical results. It was concluded that 30 m down-current Tot-P concentrations could be modelled under a diverse set of culture and environmental conditions, suggesting typical 'near-field' concentrations of Tot-P can be explained on the basis of farm activities.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".