Bibliographic record
Abstract
This research investigated the resolving of soil carbon and interconnected properties in conjunction with vegetation and landform attributes for a boreal context within the Great Clay Belt region of northern Ontario, Canada. Objectives entailing sophisticated statistical inference of extracted soil samples, an endemic tree species classification and enhanced digital soil mapping of carbon were explored, yielding research contributions and practicable results. Statistical inference was achieved by adapting generalized estimating equations (GEEs) to estimate and assess refined mean differences of soil carbon between specific land cover types and tree species groupings. Furthermore, the feasibility of GEEs for digital soil mapping was demonstrated by predicting carbon concentrations with associated confidence intervals. Regarding boreal vegetation modeling, a pixel-based tree species classification was developed for natural settings at the remarkable spatial resolution of 2 m over an area of 100 square km. Afterwards, digital soil mapping of carbon was implemented by employing data-driven approaches via encoder-decoder (ED) neural networks formulated for relatively smaller data sets. Using EDs, modeling accuracy for soil carbon was augmented with coefficients of determination (R-squared) increasing to over 0.5. Finally, knowledge-based approaches to unravel linkages between soil carbon and interrelated properties were accommodated through structural equation modeling (SEM). A framework was devised to effectively facilitate SEM for digital soil mapping. The SEM quantified impacts on soil properties from covariates relating to soil formation factors, supporting the discernment of vegetational and environmental drivers for bulk density, carbon and carbon-to-nitrogen ratio. Uncertainty with prediction was also ascertained. A normalized entropy metric constituted from the top two contending groupings was conceived, which better encapsulated prediction uncertainty when compared to conventional entropy. Quantile mapping was also incorporated to uncover insights regarding prediction uncertainty from model ensembles. Structured query language (SQL) was harnessed to efficiently derive and generate rasterized covariates from LiDAR point cloud data. This novel computation consisted of a more detailed digital terrain model (DTM), as well as covariates pertaining to vegetational structure with canopy height model (CHM) and a gap fraction. These covariates were successfully exploited as predictors for modeling with both digital soil mapping and tree species classification.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".