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Record W2072197996 · doi:10.1121/1.4919987

Mobility of dinoflagellates measured by high-frequency ultrasound

2015· article· en· W2072197996 on OpenAlexaff
Hansoo Kim, Tae‐Hoon Bok, Kweon-Ho Nam, Juho Kim, Dong‐Guk Paeng, So-Jeong An, Joon-Baek Lee

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDinoflagellatePhytoplanktonBenthic zoneChristian ministryOceanographyPhotosynthesisHigh frequency ultrasoundSedimentationUltrasoundEnvironmental scienceRed tideBiologyPhysicsEcologyGeologyBotanyAcousticsNutrient

Abstract

fetched live from OpenAlex

The importance of phytoplankton contributing more than 50% of the global amount of photosynthesis has been emphasized for a long time. Sometimes, the over-growth of phytoplankton causes the negative influence such as red-tide phenomenon on marine ecological environments. Therefore, the measurement of the mobility of phytoplankton is important. In this study, the mobility of the benthic dinoflagellate, Amphidinium carterae Hulburt (A. Carterae) incubated by f/2 medium was investigated using high-frequency ultrasound. Backscattering signal from A. Carterae was measured for 2 s in every 2 min by a 40-MHz ultrasound transducer, and the integrated backscattering power calculation was followed. The mobility of A. carterae in response to the light was illustrated by M-mode image of the echoed signals. The mobility of A. carterae was estimated to about 0.4 mm/s for the upward movement in response to light, while its sedimentation rate was measured to about 0.1 mm/s in a dark environment. This study suggests that mobility of benthic dinoflagellates responding to light can be measured by M-mode imaging of high-frequency ultrasound. (This research was a part of the project titled “Measurement of cells division and photosynthesis of phytoplankton using ultrasound”, funded by the Ministry of Oceans and Fisheries, Korean.)

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)0.002
metaresearch head score (Gemma)0.001
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 score0.074
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.251
Teacher spread0.226 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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