Comments on Halat L., Galway M.E., Gitto S. & Garbary D.J. 2015. Epidermal shedding in<i>Ascophyllum nodosum</i>(Phaeophyceae): seasonality, productivity and relationship to harvesting.<i>Phycologia</i>54(6): 599–608.
Bibliographic record
Abstract
:Halat et al. (2015 Halat L., Galway M.E., Gitto S. & Garbary D.J. 2015. Epidermal shedding in Ascophyllum nodosum (Phaeophyceae): seasonality, productivity and relationship to harvesting. Phycologia 54: 599–608[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]. Phycologia 54: 599–608) presented data collected on the epidermal shedding of the intertidal alga Ascophyllum nodosum (Phaeophyceae, Fucaceae) in Nova Scotia, Canada. An analysis of the published data and their interpretation leads us to conclude that this publication includes some serious errors of fact and interpretation that were not identified during the review process and which may prove damaging to the A. nodosum harvesting industry in the North Atlantic. The main issues identified in Halat et al. (2015) Halat L., Galway M.E., Gitto S. & Garbary D.J. 2015. Epidermal shedding in Ascophyllum nodosum (Phaeophyceae): seasonality, productivity and relationship to harvesting. Phycologia 54: 599–608[Taylor & Francis Online], [Web of Science ®] , [Google Scholar] are (1) The data collected do not provide information on the rate of epidermal shedding and certainly do not warrant the 1% monthly value suggested by the authors; (2) The authors' assumption that the reproductive material represents 100–140% of the vegetative tissues is not representative, and the references cited by the authors do not support their claims; and (3) The authors make a fallacious and erroneous statement when they claim that the ‘industrial harvest may be accounting for no more than half of the biomass that is actually being removed from the environment’.
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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 | 0.009 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.071 | 0.061 |
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".