Root harvester machine: a review of papers from the Scopus database published in English for the period of 1982-2022
Bibliographic record
Abstract
Agricultural products, including root fruits, make up a large part of a person’s vital needs. Therefore, cultivating root fruits and harvesting crops without harm is one of the main tasks of agricultural events. Considering the above, it is of great importance to have information about the scientific research and scientific results achieved by our scientists in this field. To this aim, a bibliometric analysis of articles on root harvesters published in the Scopus database between 1982 and 2022 was used to understand the current state of studying cultivating agricultural products, including root fruits, and harvesting their crops and to provide references for future studies. To carry out this research different tools such as Office Excel 2021, VOS Viewer and Mapchart.net were used. The literature retrieved totaled 201 articles, of which 70% were research papers. During the last four decades, the quantity of published papers has increased significantly. For example, there were 22 papers published in 2019, 22 times increase over the number of papers published in 2002 (1 paper). It was found that the top five countries that published the most literature were China, the United States, India, the United Kingdom, and Canada, which published 44, 43, 12, 12, and 10 articles, respectively. During the chosen period 159 authors from 58 countries contributed to the given field.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.041 | 0.043 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".