World Soil Information Service (WoSIS) - Towards the standardization and harmonization of world soil data. Procedures Manual 2018
Bibliographic record
Abstract
To better address the growing demand for soil information ISRIC - World Soil Information has developed a centralized database for the shared benefit of the international community. This database, hereafter referred to as WoSIS (World Soil Information Service), has been designed in such a way that, in principle, any type of soil data (point, polygon, and grid) may be accommodated. However, WoSIS will only provide quality-assessed data in a consistent format, with detailed information on data lineage and conditions for use. Data derived from WoSIS may be used to address pressing challenges of our time including food security, land degradation, water resources, and climate change. At present, the focus in WoSIS is on developing consistent procedures for standardizing and harmonizing soil analytical data as submitted by a wide range data providers. The general procedure for processing profile data in WoSIS is as follows. First, new source data are imported ‘as is’ into a PostgreSQL database, with the original naming and coding conventions, abbreviations, domains, lineage and data licence; thereby copies of the source materials are safeguarded at ISRIC. Second, the source databases are imported into WoSIS proper, forming the first major step of data standardization (into a single data model). The next step of data standardization, applied to the values for the various soil properties as well as to the naming conventions themselves, is needed to make the data queryable and useable. Special attention has been paid to the standardization of analytical method descriptions, focusing on the list of soil attributes considered in the GlobalSoilMap (GSM, 2013) specifications (e.g. organic carbon, soil pH, soil texture (sand, silt, and clay), coarse fragments, cation exchange capacity, bulk density, and water holding capacity), to which we have added electrical conductivity. Further, we checked and added the soil classification (FAO, WRB and USDA Soil Taxonomy) and horizon designations as provided in the source databases. During the standardization of the analytical method descriptions, major characteristics of commonly used methods for determining a given soil property are identified first. For soil pH, for example, these are the sample pretreatment, extractant solution (water or salt solution), and in case of salt solutions the salt concentration (molarity), as well as the soil/solution ratio; a further descriptive element is the type of instrument used for the actual laboratory measurement. Similar schemes were developed for the other soil properties under consideration here, with accompanying flowcharts. A third step in the standardization / harmonization process will require data harmonization to make the analytical data comparable that is as ’if assessed by a single given (reference) method’. Such work will require further international collaboration and data sharing to the benefit of the international user community as foreseen in the framework of Pillar 5 of the Global Soil Partnership. Inherently, the present standardization procedures are only applied to soil profiles flagged as having adequate permissions (i.e. ’shared’ profiles with at least a Creatice Commons Licence type CC BY or CC BY-NC). The resulting standardized data can be accessed through our GeoNetwork instance (http://data.isric.org/). The latest, dynamic dataset is available through a web feature service (WFS); the corresponding data layers are referred to as ‘WoSIS latest’. For consistent citation purposes, we also produce ‘static’ snapshots of the standarized data in comma delimited format (CSV), most recently ‘WoSIS snapshot - July 2016’ (Batjes et al., 2017). WoSIS forms an important building block of ISRIC‘s Spatial Data Infrastructure (SDI). Further developments will allow for the fulfilment of future demands for global soil information, and enable further incorporation of soil data shared by third parties in an inter-operable way, within a federated system. www.isric.org
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.101 | 0.158 |
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".