Harvest Recovery of a North Atlantic Intertidal Seaweed, Ascophyllum nodosum: Experimental Design Issues
Bibliographic record
Abstract
As the global demand for seaweed products increases, resource managers, conservation groups, and other stakeholders strive to protect wild seaweed populations and the ecosystem services they provide from the damaging effects of over-harvesting. Ascophyllum nodosum (rockweed) is a slow-growing, intertidal brown alga of the North Atlantic that is commercially harvested for crop biostimulants, soil conditioners, and other products. Rockweed is considered a foundation species due to its high abundance, tall canopy, habitat characteristics, and role in detrital food webs. Rockweed shoots survive after harvesting if the holdfast remains intact, but rates of canopy and biomass recovery depend on the intensity of harvesting. In Maine, USA, and eastern Canada, little is known about how harvesting rockweed at various intensities affects recovery rates of algal height or biomass. Herein, we evaluate published studies and suggest improved experimental designs. Most experimental studies focus on a single harvest event, often with incomplete data on control plots, amount of biomass removed, or previous harvesting history at study sites. Much has been learned from previous work, but more rigorous studies are needed to develop harvest recommendations that address both commercial and conservation-related goals. Importantly, experimental studies of the effects of repeated harvesting on rockweed beds are lacking.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".