Application of multidimensional soil data harmonization to develop the Ontario Soil Information System (OSIS)
Bibliographic record
Abstract
In the digital age, soil data have become crucial for understanding the role of soil in agricultural systems, biodiversity, carbon sequestration, ecosystem services, and sustainability, thus guiding decision-making. However, diverse data collection methods and fragmented soil data management practices complicate creation of a unified soil database from varied datasets. In Ontario, Canada, data fragmentation across different institutions hinders access and use of this vital soil data for spatial and temporal analyses. Moreover, the latest update in the National Pedon Database is dated back to 2011, underscoring the need for centralized provincial data warehouses for systematic soil information access and analysis. This study addressed these challenges by developing and implementing an end-to-end, multidimensional soil data curation framework that integrates diverse soil data genres and sources in Ontario, enhancing the database updating process. Applying minimum inclusion criteria, data from 13 sources across four different data genres (fixed depth, topsoil, profile, and peatland), comprising 14 145 observation sites and their respective layers, were integrated into an SQLite database. Despite dense sampling in southern Ontario, data gaps increased with depth. Harmonization focused on key attributes such as depth, soil organic carbon, and texture yielded 6335 unique sites and 28 134 layers, including 5460 profiles, 178 topsoil, and 697 fixed depth sites. Using a scalable and transparent soil data “hyperlooping” framework, integrated with platforms like KNIME, R, and SQL, this comprehensive database supports enhanced digital soil assessment and mapping in Ontario, and beyond.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".