Bibliographic record
Abstract
The quality of near-shore coastal waters and estuaries is of great concern to North Americans, particularly as these ecosystems become increasingly threatened by pollution. An improved understanding of chemical impacts on near-shore ecosystems is essential to responsible stewardship of these coastal areas. In recent years, the aquaculture industry has become a major contributor to the Canadian economy, however, this industry’s use of chemicals, including those used in disinfectants, anti-fouling paints, and feed additives has resulted in the contamination of local net pen areas. Another complication of these net-pen areas is the abundance of sea lice on the fish, which the salmon farmers in Canada and the world need to control. contamination associated with the use of therapeutants to treat sea lice has emerged as a significant problem to non-target organisms. This study specifically addresses information gaps that need to be filled in order to understand the environmental consequences of using two chemical therapeutants for sea lice treatments, Salmosan® and Paramove 50®. Zooplankton play a key role in marine food web dynamics, biogeochemical cycling, and fish recruitment, however, despite their importance in marine environments, our knowledge of the interactions between zooplankton and aquaculture therapeutants is extremely limited. This study describes scientific studies on the lethal and sub-lethal toxicity of these two therapeutants to representative marine zooplankton species under realistic exposure scenarios. The data obtained from the proposed research is required to ensure the proper and safe use, and appropriate regulation of these aquaculture chemicals in Canada.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".