Drilling within the critical zone
Bibliographic record
Abstract
The critical zone (CZ), extending from the vegetation canopy to unweathered bedrock, hosts coupled hydrologic, geochemical, and biological interactions that regulate soil health, water resources, and ecosystem sustainability. The subsurface CZ can extend tens of meters below the surface and is largely inaccessible, except in happenstance exposure from quarries or road cuts through mountain hillsides. Drilling and subsequent borehole sampling, monitoring and imaging reveal the importance of the deep subsurface for CZ evolution and function. However, drilling can be labor-intensive, require expensive, specialized equipment, and can only be done where the equipment can be deployed, limiting the number and placement of boreholes. To empower the next generation of critical zone scientists to employ drilling and downhole techniques, this review synthesizes emerging research objectives and methods commonly used during CZ drilling campaigns over the last 30 years. We focus on three CZ research themes: (1) physical and chemical weathering, (2) subsurface water storage and flow, and (3) solute, microbial, and gas dynamics. For each theme, we evaluate drilling techniques, sampling strategies, downhole logging approaches, long-term monitoring, and analytical methods that collectively enable diverse hypothesis-testing. We conclude by providing a vision for the future of drilling within the CZ, with a focus on novel drilling techniques aimed at recovering saprolitic material as well as borehole designs that can monitor and sample the vadose zone. Additionally, we emphasize that near-surface geophysics and data-model integration efforts are needed to expand borehole observations to the larger scales that are necessary to advance CZ science and inform ecosystem and water resource management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".