MétaCan
Menu
Back to cohort
Record W2355146656

Discussion the Situation of the Development of the Sea-buckthorn Dink

2014· article· en· W2355146656 on OpenAlexaff
LI Xiang-y

Bibliographic record

VenueAcademic Periodical of Farm Products Processing · 2014
Typearticle
Languageen
FieldMedicine
TopicPhytochemical and Pharmacological Studies
Canadian institutionsScience North
Fundersnot available
KeywordsBerryFood scienceBiologyBotany
DOInot available

Abstract

fetched live from OpenAlex

Loaded with nutrition,sea-buckthorn berry possesses the characteristics of high medical value. At present,the various kinds of foods and supplements which make of sea-buckthorn berries are developed,including sea-buckthorn seed oil,juice of the sea-buckthorn berry,compound beverage of sea-buckthorn berry. Sea-buckthorn also use to make special yogurt,compound nutrition meal,peanut milk,ice-cream,soy sauce,sea-buckthorn beverage,etc. Highly processed sea-buckthorn drink is one of high-valued way to process sea-buckthorn berries in China. Regarding the seasonal characteristics of sea-buckthorn and limited time of storage,sea-buckthorn maybe a excellent way to solve these problems.Comprehensive utilization of sea-buckthorn high-tech and deep processing can be promoted by this technology.

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)0.002
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 score0.249
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.314
Teacher spread0.277 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAcademic Periodical of Farm Products ProcessingSame topicPhytochemical and Pharmacological StudiesFrench-language works237,207