Coupling salinity reduction to aquatic animal well-being and ecosystem representativeness at the Biodôme de Montréal
Bibliographic record
Abstract
This paper presents a case study of a locally adapted sustainable strategy of salinity reduction applied to the Saint Lawrence maritime ecosystem at the Biodome de Montreal. In conformity with the standards of the CAZA (Canadian Aquarium and Zoos Association), this procedure was implemented to reconcile animal well-being, ecosystem representativeness and control of costs under the operational environment of a cold seawater recirculation system featuring the Golfe du Saint Laurent Ecosystem (GSLE) and its associated live collection. A simple methodology to carry out safe salinity reduction procedures of artificial seawater environments (from 28 to 24 Practical Salinity Units) is proposed and detailed. Adapted salinity challenge tests at 14, 21 and 24 were conducted beforehand and simple adapted indicators were used on a selection of key species (thorny skate: Raja radiate ; little skate: R. erinacea ; barndoor skate: R. laevis ; Atlantic cod: Gadus morhua ; green urchins: Strongylocentrolus droebachien and American lobster: Homarus americanus ) to evaluate the well-being and mortality risks associated with both a lower operational salinity (long-term exposure) and an unavoidable salinity drop (short-term exposure) observed during routine large-scale water renewal operations. Economic gains achieved through reduction in the use of costly synthetic salt formulation were calculated. The savings achieved during three years of operation at 24 PSU have been applied to the improvement of the water quality control management capacities of the GLSE exhibit such as a sulphur-based denitrification unit, additional ozonation and protein skimming capacities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".