Feasibility of salmon farming as a small business in British Columbia
Bibliographic record
Abstract
The hypothesis was that salmon farming can be the basis for the establishment of a viable small business in British Columbia. The constraints on the establishment of a salmon farm imposed by government regulations, the availability of funds, and the market for pan-size salmon were presented and discussed. The pertinent biological knowledge associated with the rearing of captive salmon was summarized. A simple production model designed to produce approximately one-half million marketable, pan-size salmon within 15 months was derived from published accounts of studies at experimental fish farms. A hypothetical salmon farm was described in detail including estimates of the capital and operating costs associated with the farming and processing activities. The required information was obtained through interviews with knowledgeable members of the industry. The estimated revenues and expenses attributed to the hypothetical farm were analysed using pro forma financial statements to ascertain the financial position and the net cash flow which may be expected. The profitability of the farm was analysed by applying net present value and internal rate of return criteria to the net cash flow. Sensitivity analysis of the effect of changes to the product mix. market price, labour rate, feed price, tax rate, and stocking density upon the profitability of the farm was conducted. Estimates of the critical values of the above parameters which would allow the farm to be a feasible investment opportunity were derived. The conclusion is that pan-size salmon farming can be a feasible small business in British Columbia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".