Amalgamation and harmonization of soil survey reports into a multi-purpose database
Bibliographic record
Abstract
There is a growing demand for standardized, easily accessible, and detailed information pertaining to soil and its variability across the landscape. Typically, this information is only available for selected areas in the form of local or regional soil surveys reports which are difficult, and costly, to develop. Additionally, soil surveying protocols have changed with time, resulting in inconsistencies between surveys conducted over different periods. This article describes systematic procedures applied to generate an aspatial, terminologically, and unit-consistent, database for forest soils from county-based soil survey reports for the province of New Brunswick, Canada. The procedures involved (i) amalgamating data from individual soil surveys following a hierarchical framework, (ii) summarizing and grouping soil information by soil associations, (iii) assigning correct soil associates to each association, with each soil associate distinguished by drainage classification, (iv) assigning pedologically correct horizon sequences, as identified in the original soil surveys, to each soil associate, (v) assigning horizon descriptors and measured soil properties to each horizon, as outlined by the Canadian System of Soil Classification, and (vi) harmonizing units of measurement for individual soil properties. Identification and summarization of all soil associations (and corresponding soil associates) was completed with reference to the principal soil-forming factors, namely soil parent material, topographic surface expressions, soil drainage, and dominant vegetation type(s). This procedure, utilizing 17 soil surveys, resulted in an amalgamated database containing 106 soil associations, 243 soil associates, and 522 soil horizon sequences summarizing the variability of forest soil conditions across New Brunswick.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".