MétaCan
Menu
Back to cohort
Record W2054694966 · doi:10.1007/s11745-001-0409-6

Protein‐precipitating capacity of crude condensed tannins of canola and rapeseed hulls

2001· article· en· W2054694966 on OpenAlexaff
M. Naczk, Ryszard Amarowicz, R. Zadernowski, Fereidoon Shahidi

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsMemorial University of NewfoundlandSt. Francis Xavier University
Fundersnot available
KeywordsCanolaRapeseedTitrationChemistryTitration curveChromatographyProtein precipitationAbsorbanceBovine serum albuminPrecipitationBrassicaAnimal scienceFood scienceBotanyBiologyOrganic chemistryHigh-performance liquid chromatography

Abstract

fetched live from OpenAlex

Abstract The protein‐precipitating potentials (PPP) of soluble condensed tannins (SCT) were determined in hulls from several samples of canola and rapeseed varieties. The PPP were expressed as slopes of lines (titration curves) reflecting the amount of SCT‐protein precipitated vs. the amount of SCT added to the reaction mixture. The slopes ( S p ) of titration curves obtained using the protein‐precipitation assay ranged from 2.96 to 10.91 (absorbance units at 510 nm per mg SCT), and those of titration curves, obtained using the dye‐labeled bovine serum albumin (BSA) assay ( S d ), ranged from 28.1 to 267 (% precipitated BSA per mg SCT). For both assays, a statistically significant ( P ≤0.001) semilogarithmic linear correlation existed between the slopes and the SCT contents in the canola and rapeseed hulls. Higher amounts of SCT‐protein complexes were precipitated at 40°C than at room temperature. Determination of titration curves under standardized conditions (type and concentration of protein, pH and temperature) afforded meaningful differences in the slopes among the range of SCT extracts from canola and rapeseed hulls used in this study.

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 score0.074
Threshold uncertainty score0.146

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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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

Citations12
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Oil Chemists SocietySame topicPhytase and its ApplicationsFrench-language works237,207