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Record W3005808599 · doi:10.3389/fevo.2020.00020

Management and Exploitation of Macroalgal Biomass as a Tool for the Recovery of Transitional Water Systems

2020· article· en· W3005808599 on OpenAlexfundno aff
Adriano Sfriso, Michele Mistri, Cristina Munari, Alessandro Buosi, Adriano Sfriso

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
FundersEnvironmental Studies Research Funds
KeywordsBiomass (ecology)EcologyEcosystemBenthic zoneEnvironmental scienceAquatic ecosystemAnoxic watersBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.097

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.173
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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