Reply to the comment by Payne et al. on “Does the fall phytoplankton bloom control recruitment of Georges Bank haddock, Melanogrammus aeglefinus, through parental condition?”Appears in Can. J. Fish. Aquat. Sci. <b>65</b>: 1076–1086.
Bibliographic record
Abstract
Payne et al. (Can. J. Fish. Aquat. Sci. 66: 869–872, 2009) raised several points concerning the handling and interpretation of data that went into an analysis of the population dynamics of Georges Bank haddock that suggested a relationship between the fall phytoplankton bloom and recruitment (Can. J. Fish. Aquat. Sci. 65: 1076–1086, 2008). Their main points were the manner in which logarithmic transforms were applied, whether the 2003 year class was truly as large as estimated in a 2006 assessment, and if correlation analyses of zooplankton data should be reconsidered. The reply to these comments was aided by a new assessment which provided additional years of data and improved the quality of the recruitment time series. The reply analyses showed that the relationships were robust to the way the logarithmic transform was applied, the initial estimates of the size of the 2003 year class were correct, and relationships between recruitment and spring zooplankton biomass levels remain statistically insignificant. From these new analyses, the interpretations and conclusions reached in the original paper remain the same; the fall bloom has emerged as a candidate explanatory variable for the stock independent variation in haddock recruitment on Georges Bank.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.041 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".