Review on Oil Content and Bio-chemical Components of Different Provenances of seabuckthorn
Bibliographic record
Abstract
To introduce and breed superior seabuckthorn species with high oil content and rich in special bio chemical components,on the basis of sorting out and analyzing the data in China and abroad oil content and bio chemical components of the different species were compared in this paper.The results indicated that:(1)Superior seabuckthorm could be introduced and bred by industrial need, in industrial production suitable processing raw material could be selected according to uses of the oil.(2)Of 13 species compared species oil contents of seeds of H. thibetana Schlecht in Sichuan and Qinghai province and seabuckthorn in Shaaxi are higher 19.51%,14.57% and 10.37% respectively,oil contents of pulp of Subsp. neurocarpa of Qinghai,seabuckthorn of Kazakhstan and Subsp. turestanica Rousi of Uloumuqi are higher 34.26%,23.01% and 22.57% respectively; oil contents of No.K 24 and Buliyate of new species bred by Russia reach 32.60% and 11.67%～36.40%.(3)Of 6 species compared, vitamin E content of Canadian seabuckthorn is the highest, H. thibetana Schlecht. Subsp. neurocarpa and Subsp. sineses Rousi of China are also higher,of 5 comparative species, non saponification and sterol conents of seed oil of Subsp. sineses Rousi are the highest, Subsp. neurocarpa second, about pulp oil, H. thibetana Schlecht first, Russion Seabuckthorn second, of 4 compartive species,carotenoid comtents of seed and pulp oil of Subsp.sineses Rousi is the highest. This provids a theoretical basis for selecting resources of seeds of introducing and breeding superior seabuckthorn species, and plays an important role in setting up high quality plantation and developing industry of seabuckthorn high beneficially and sustainably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".