MétaCan
Menu
Back to cohort
Record W19667774 · doi:10.12927/hcq.2009.20963

Влияние эдафических факторов Терско-Сулакской низменности и горного Хунзахского района Дагестана на нутриентный состав шиповника Rosa Canina

2011· article· en· W19667774 on OpenAlexaboutno aff
Котенко Марина Евгеньевна

Bibliographic record

VenueПолитематический сетевой электронный научный журнал Кубанского государственного аграрного университета · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAscorbic acidManganeseEcologyBiologyBotanyEnvironmental scienceGeographyChemistryHorticulture

Abstract

fetched live from OpenAlex

The characteristics of meadow-wood typical soils, located on territory of lowland of Tersko-Sulaksk and Hunzahsk mountain area of Dagestan are given. Results of research of the contents of vitamin C (an ascorbic acid), P-active connections and mineral substances of iodine, manganese, copper and zinc in the hips, growing on above-stated ground are resulted. The received data testify that the meadow-wood ground of Tersko-Sulaksk lowland promotes accumulation of iodine in a dog rose, and the similar mountain ground in a greater measure influences synthesis of vitamins and the contents in hips of mineral substances. The value researched biochemical connections for a human's organism of the person, the is shown and the comparative estimation of their structure in a dog rose growing on meadow-wood grounds, being on various heights above sea level is given

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.048
GPT teacher head0.157
Teacher spread0.110 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueПолитематический сетевой электронный научный журнал Кубанского государственного аграрного университетаSame topicSoil and Environmental StudiesFrench-language works237,207