Bibliographic record
Abstract
Introduced forage species may be necessary to improve forage production and stabilize the soils of desert steppe.From 2000 to 2004,23 perennial gramineous forage varieties and materials from the United States,Canada,and China were evaluated by plot experiments with random design on their adaptation,productivity,and forage quality in Stipa klemenzii Roshev.desert steppe in Suniteyou Banner,Xilinguole League,Inner Mongolia Autonomous Region,P.R.China.10 m2 plot was employed with 5 replicates for each variety.The results show that the overwinter survival rates of Agropyron mongolicum Keng cv.'Neimeng',A.desertorum(Fisch.) Schult.,A.mongolicum Keng (a native species),A.mongomicum Keng.(a new strain),Psathyrostachys juncea(Fisch.) Nevski cv.'swift',P.juncea(Fisch.) Nevski(P3),and P.juncea(Fisch.) Nevski cv.'Bozoisky' were above 80%.Those grasses had higher forage yields and higher crude protein contents than others and produced more root biomass that were distributed to a greater depth and spread over a wider horizontal distribution.The adaptability,high productivity,and high quality of those grasses would make them suitable for more extensive use in the desert steppe area.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".