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Record W4293549039 · doi:10.15578/squalen.633

A Review on The Biological, Physical and Chemical Mitigation of Harmful Algal Bloom

2022· review· en· W4293549039 on OpenAlexaff
Nur Hanisah Ibrahim, Anwar Iqbal, Normawaty Mohammad‐Noor, Roziawati M.R., Dede Heri Yuli Yanto, Lee D. Wilson, Abdul Hanif Mahadi

Bibliographic record

VenueSQUALEN Bulletin of Marine and Fisheries Postharvest and Biotechnology · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Saskatchewan
FundersUniversiti Sains Malaysia
KeywordsAlgal bloomHarmBiochemical engineeringEnvironmental scienceRisk analysis (engineering)Human healthBusinessNatural resource economicsEnvironmental resource managementEnvironmental planningComputer scienceEcologyEngineeringBiologyEnvironmental healthMedicinePolitical scienceEconomicsPhytoplankton

Abstract

fetched live from OpenAlex

The harmful algal bloom (HABs) refers to the rapid growth of toxic or high-biomass-producing microalgae. The impact of this phenomenon can cause significant economic loss affecting many industries and causing harm to wildlife and human health. As technology develops, greater research has been conducted to monitor and reduce HABs occurrence’s impact, including mitigating agents. This review presents the advantages and disadvantages of currently used and recently developed biological, chemical, and physical approaches to tackle issues related to HABs. The present review also emphasizes the interaction between the mitigating agents and the algal cells, thus identifying the gap of knowledge that needs to be addressed. Understanding the advantages and disadvantages of the approaches and the interaction between the mitigating agents and algal cells will enable researchers to develop a better sustainable system for managing HAB.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.236
Teacher spread0.215 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2022
Admission routes1
Has abstractyes

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