Bibliographic record
Abstract
On the basis of thorough survey on campus plants in 4 universities in Ningbo areas,this paper analyzes the plants in terms of assortment,number,planting frequency,species richness,etc.The investigation results show that there are 207 types of plants identified in the universities,belonging to 90 families and 199 genera,among which 45 species of arbors accounting for 22%,45 species of bushes for 22%,13 species of Liana for 6%,and 104 species of herbs accounting for 50%.There are two types of plant per hectare on average and the richness index is 16.86.111 species are found in ornamental plants making up 54%;87 species of weed dominates in number and account for 79% of herbal and 40% of the total number of plants,including the Japan Hop,Canada Goldenrop and Goosegrass.All the weeds inflict serious undesired impacts on other plants and compromise the scene of campus landscape.It is found that,although the plant resources on campus seem plentiful as a whole,the greenery structure needs to be enhanced by greater diversities,and the visual effects still have much room for improvement.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".