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Record W3040131329 · doi:10.1017/s1431927600036576

Cell Boundary Detection and Volume Approximation of Confocal Microscope Images for Bioinformatics

2000· article· en· W3040131329 on OpenAlexaff
Cybelle Fernandez, Damiaan F. Habets, Stefan C. Kremer, Marc Le Maguer

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPlasmolysisOsmotic dehydrationProtoplastTonicityCytoplasmDehydrationConfocal microscopyConfocalBiological systemMicroscopeComputer scienceBiophysicsMicroscopyChemistryProcess (computing)Materials scienceCell wallCell biologyBiologyBiochemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Bioinformatics is the science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research. This paper will detail the application of a three-dimensional cell boundary detection algorithm to compute the volumes of osmotically dehydrated apple cells in an effort to better understand the effects of this treatment. Osmotic dehydration is an efficient pre-treatment technique in food processing. However, the changes that occur in the food material at the cellular level have not been thoroughly understood and this has limited the full industrial application of the process. During osmotic dehydration, plant tissues when placed in hypertonic solutions undergo plasmolysis that can be directly observed under light microscopy. The usual phenomenon observed during plasmolysis is the separation of the cytoplasm from the cell wall, due to the removal of water from the protoplast (Frey-Wyssling and Muhlethaler, 1965). Although readily observed under the microscope, however, the quantitative changes that occur during the process are not easily measurable.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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 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

Citations0
Published2000
Admission routes1
Has abstractyes

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