Possible fish stock size assessment and available production survey as developed on Lake Kariba
Bibliographic record
Abstract
This paper is an outline of methods practically useful for the evaluation of ichthyomass, fish abundance, available production and yield in lakes and rivers.Terms and concepts are reviewed, and difficulties stemming from the use of "predetermined" mathematical models are discussed. Sampling with toxicants in blocked-off areas was found to be the most practical method and is described in detail. For the total estimation of ichthyomass the spatial ranges of fish distribution must be determined; the results of echo-sounding surveys for horizontal, vertical, topographical, seasonal and diel fish distribution are given.Some of the most important methods for computing available production are listed and applied to Lake Kariba as an example. In particular, a method based on the balance between the main predator and prey species is reviewed. The ecological production survey concept is finally stressed as applied to multispecies fish stocks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads agree on what is shown here.
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".