MétaCan
Menu
Back to cohort
Record W2897286657 · doi:10.15353/jcvis.v4i1.324

SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae

2018· preprint· en· W2897286657 on OpenAlexvenueno aff
Jason Deglint, Yitian Wang, Chao Jin, Alexander Wong

Bibliographic record

VenueJournal of Computational Vision and Imaging Systems · 2018
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAlgaeAbsorption (acoustics)FluorescenceMultispectral imageSample (material)OpticsChlorophytaMaterials scienceFluorescence microscopeMicroscopyComputer scienceOptoelectronicsBiological systemRemote sensingArtificial intelligenceChemistryBotanyPhysicsBiologyGeology

Abstract

fetched live from OpenAlex

This paper presents SAMSON, a Spectral Absorption-fluorescenceMicroscopy System for ON-site-imaging of algae within a watersample. Designed to be portable and low-cost for on-site use,the optical sub-system of SAMSON consists of a mixture of low-cost optics and electronics, designed specifically to capture bothfluorescent and absorption responses from a water sample. Thegraphical user interface (GUI) sub-system of SAMSON was de-signed to enable flexible visualisation of algae in the water samplein real-time, with the ability to perform fine-grained exposure con-trol and illumination wavelength selection. We demonstrate SAM-SON’s capabilities by equipping the system with two fluorescentillumination sources and seven absorption illumination sources toenable the capture of multispectral data from six different algaespecies (three from the Cyanophyta phylum (blue-green algae) andthree from the Chlorophyta phylum (green algae)). The key benefitof SAMSON is the ability to perform rapid acquisition of fluores-cence and absorption data at different wavelengths and magnifica-tion levels, thus opening the door for machine learning methods toautomatically identify and enumerate different algae in water sam-ples using this rich wealth of data.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 designSimulation or modeling
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Computational Vision and Imaging SystemsSame topicWater Quality Monitoring and AnalysisFrench-language works237,207