The David Noakes article that debunked the misguided belief that absolute numbers of fish can be captured in fresh waters: a lesson for early-career scientists
Bibliographic record
Abstract
Abstract This article presents a personal account of the important contribution a publication from David Noakes’ lab (Pot, Noakes, Ferguson and Coker 1984, Quantitative sampling of fishes in a simple system: failure of conventional methods. Hydrobiologia 114:249–254) made to freshwater fish science in general and to the successful public defence of a doctoral thesis in particular. Pot et al. (1984) tested the accuracy of two conventional sampling approaches in their estimation of numbers of fish in a small pond (capture-mark-recapture and total sampling, following rotenone treatment). Their results demonstrated that even in a small and relatively uniform freshwater system (a pond of 0.1 ha), the so-called total sampling approach failed to provide the true number of any species of fish in the pond. The outcome of the study provided the evidence to debunk the assumption that absolute numbers of fishes can be obtained using rotenone treatment. This article therefore allowed me to defend my doctoral dissertation in the face of critical comments from a principal jury member, and firm proponent of ‘absolute sampling’, and it provided fish biologists with justification to focus on the development and application of sampling approaches, such as relative densities, which do not require futile attempts to obtain total fish numbers.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".