Peer Review #4 of "Contents and yields of copper, iron, manganese and zinc would be affected by lucerne age and cut (v0.1)"
Bibliographic record
Abstract
Background.Lucerne is a perennial legume forage, which can produce multiple cuts in one year.Microelements play fundamental roles in the function, maintenance and adaptation to the environment for lucerne growth.However, it is unknown previously on the accumulation of copper (Cu), iron (Fe), manganese (Mn) and Zinc (Zn), which vary with lucerne ages or cuts.Therefore, a hypothesis on the Cu, Fe, Mn and Zn in lucerne varying with age and cut, was tested.Methods.11, 8, 5, 4 and 1 year old lucerne (Medicago sativa Longdong) were selected as the material (until 2012 year), and samples were taken as three cuts at the cutting periods (early flowering stage) in 2012.Then, the contents and yields of Cu, Fe, Mn and Zn in lucerne were measured and calculated.Results.The highest contents of Cu, Fe, Mn and Zn in lucerne were found in the 1 year old among the five ages, at the 3 rd cut compared to the other two cuts, and in the leaf among the three organs.The highest yields of Cu, Fe, Mn and Zn were found in the older ages (11 and 8 years old), at the 3 rd cut, and in the root among the three organs.The most positive correlations were found between contents, yields and biomass.Conclusions.The hypothesis was supported by the results.And the contents and yields of lucerne Cu, Fe, Mn and Zn were affected by the age, cut and organ.Furthermore, the yields of lucerne Cu, Fe, Mn and Zn were determined by their contents and lucerne biomass.
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.393 | 0.253 |
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".