A Modular Aquaculture Modelling System (MAMS) And ItsApplication To The Broughton Archipelago, British Columbia (BC)
Bibliographic record
Abstract
The British Columbia Ministry of Agriculture, Food and Fisheries has undertaken the development of a numerical model to support the decision making process concerning the licensing of aquaculture sites by assessing the local and regional impacts of aquaculture operations. The prototype modelling system is comprised of three principal modules. The first is a preprocessor to establish the scenario to model; the second is a processor to coordinate the execution of the sub-modules that simulate a range of biophysical processes; and the third is a post-processor to display the results. A Windows based Graphical User Interface, a Geographic Information System, and an on-line support document interconnects these three modules. At present this modular aquaculture modelling system (MAMS) has submodules to simulate two-dimensional hydrodynamics, water quality. fish growth, and sedimentation. MAMS can provide managers with a tool to examine and communicate the complex interaction of chemical, physical and biological processes that are relevant to salmon aquaculture in the Broughton Archipelago.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".