Bibliographic record
Abstract
The review article is devoted to the analysis of the practical experience of famous world centers for the collection of soil information in databases and soil information systems. The article examines the global experience of information provision of soil research and implementation of information technologies for soil resource management. The importance of developing databases of soil parameters and properties using methods standardized and harmonized with international ones is indicated. The main concepts of soil information systems are outlined and their classification by activity levels is given. A set of criteria has been defined that outlines the purpose of creating a soil information system, its structure, the set of data that should be supplied to it, the degree of their availability to users and the degree of applicability in soil management. An overview of the largest soil information systems in the world was conducted, the principles of their construction, functioning and ways of interaction with each other were determined. The largest soil information systems are global GLOSIS (FAO GSP) and WoSIS (ISRIC), regional ESDAC (EU countries), national NASIS (United States of America), CanSIS (Canada) and ASRIS (Australia). They can be considered as the main models for building and configuring the functionality of the national soil information system, which will allow Ukraine to integrate into the international soil data exchange system. The share of our state's participation in international soil information systems and the role of the NSC "ISSAR named after O.N. Sokolovsky" in replenishing the world's soil databases have been determined. The largest number of soil profiles of Ukraine is presented in WoSIS, which receives information about soils from many national and regional databases.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".