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Record W119305598

Effects of Red Tide on Physico-chemical Properties of Water and Phytoplankton Assemblage in Sepanggar Bay, Sabah, Malaysia

2010· article· en· W119305598 on OpenAlexaff
Md. Rashed-Un-Nabi, Ee LowSin, Md. Azharul Hoque, Madihah Jafar Sidik, E Saleh, Ann Anton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsBedford Institute of OceanographyNatural Resources Canada
Fundersnot available
KeywordsRed tideBayPhytoplanktonOceanographyAlgal bloomPeriod (music)BloomEnvironmental scienceAssemblage (archaeology)BiologyEcologyGeologyNutrient
DOInot available

Abstract

fetched live from OpenAlex

Harmful algal bloom (HAB) produced by Cochlodinium sp. is a serious concern in the west coast of Sabah in Malaysia. Recently frequent occurrence of red tide has been reported in Sepanggar Bay, south of Kota Kinabalu. This paper presents a comparative study of physico-chemical properties of water and phytoplankton assemblage during red tide and non-red tide period in Sepanggar Bay. Measured parameters were significantly different (p<0.05) between the red tide and non-red tide periods. The mean abundances of phytoplankton was 0.388x10 6 cells L and 1.628x10 6 cells during red tide and non-red tide period respectively. The global R value, obtained through analysis of similarity test, in non-red tide period (0.03) were higher than in the red tide period (0.01) which indicates more diverse phytoplankton assemblage during non-red tide period. Cochlodinium sp. was the most discriminating species (31.39%) during red tide period and Coscinodiscuss sp. (18.25%) during the non-red tide period. Through cluster analysis three species groups were found during red tide period while six groups were found during non-red tide period, which implies less diverse phytoplankton assemblage in the presence of red tide.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.277

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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 designBench or experimental
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

Citations4
Published2010
Admission routes1
Has abstractyes

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