Comment on “Does the fall phytoplankton bloom control recruitment of Georges Bank haddock, <i>Melanogrammus aeglefinus</i>, through parental condition?”Appears in Can. J. Fish. Aquat. Sci. <b>65</b>: 1076–1086.
Bibliographic record
Abstract
In the paper “Does the fall phytoplankton bloom control recruitment of Georges Bank haddock, Melanogrammus aeglefinus , through parental condition?”, Friedland et al. (Can. J. Fish. Aquat. Sci. 65(6): 1076–1086, 2008) examine a sizable number of hypotheses aiming to explain the recruitment patterns observed in Georges Bank haddock. The authors focus on a correlation between the size of the autumnal phytoplankton bloom and the survivor ratio (recruitment), concluding this to be the main factor determining recruitment, via the mechanism of adult condition at the time of spawning. Here we examine this result in close detail and re-analyse some of the data presented in the paper. We show that the recruitment metric upon which Friedland et al. base their conclusions inadvertently biases the analysis in favour of high recruitment events and against low recruitments. As a consequence, Friedland et al. disregard correlations that are, in fact, significant. Furthermore, we show that the parental condition hypothesis hinges upon a single, highly uncertain data point, without which the correlation is no longer significant. We find that evidence for the parental condition hypothesis is weak, and that in performing the analysis in the chosen manner, Friedland et al. have overlooked alternative hypotheses.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".