Management and Exploitation of Macroalgal Biomass as a Tool for the Recovery of Transitional Water Systems
Bibliographic record
Abstract
Aquatic angiosperms favor the development of ecosystems services, the welfare of marine organisms and people. Generally the presence of angiosperms in transitional systems are indicators of good ecosystem status. Presently, these environments are densely populated and often are so highly degraded that angiosperms have almost disappeared, replaced by tionitrophilic macroalgae responsible of anoxic events that deteriorate the environment furtherly. Although this trend is hardly reversible because the anthropogenic impact is increasing and the restoring of damaged environments within a reasonable time is difficult, recent studies have shown that by managing the harvesting of the natural algal species of commercial interest a progressive environmental recovery is achievable. Biomass-harvesting can contribute both to the removal of high amounts of nutrients and the generation of economic revenues for a sustainable, self-financed environmental restoration. In fact, unlike clam-farming which destroys the seabed and re-suspends large amounts of sediments, the proper management of the macroalgal biomass, can favour the nutrient abatement and the recolonization of aquatic angiosperms which help restore the conditions necessary for the conservation of the benthic and fish fauna and birds, and produce valuable economic resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".