The importance of zeros in digital soil mapping I: a review
Bibliographic record
Abstract
The presence and influence of excess zeros has been understudied and inconsistently applied within the digital soil mapping (DSM) field. Other disciplines have identified the challenges associated with fitting empirical models when the data are highly zero-inflated and have contributed to the development of modelling frameworks that can be employed to overcome them. This paper presents the need to address zero-inflation (ZI) and two of the primary methodologies currently used: zero-inflated models and two-part frameworks. The selection of which modelling framework to use relies primarily upon the researcher understanding the source of ZI within their data set, a frequent challenge when using legacy data in DSM. Several examples of properties where ZI has been recognized or is likely to occur in DSM are detailed, including depth to bedrock, coarse fragment content, horizon thickness, soil inorganic carbon, soil contaminants, and soil organisms. Ultimately, the review of ZI within DSM literature revealed that few papers have employed modelling strategies to handle excess zero values, some apply the frameworks inconsistently, and no papers directly address the source of zero observations. Future areas of research are introduced including the integration of machine learning into ZI frameworks, the suitability of ZI models as a reference to identify possible false zeros, specific use cases in spatial applications, and the use of ZI in non-target observations that may be encountered in bulk soil sampling for several properties.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".