On the Fundamental Causes of High Environmental Alkalinity (pH ≥ 9): An Assessment of Its Drivers and Global Distribution
Bibliographic record
Abstract
Abstract Very alkaline environments exceeding calcite buffering are globally rare but conspicuous in many sedimentary plains of the World. While the deleterious effects of high alkalinity on soils are well understood, less agreement exists on its causes. We revise these causes to understand these exceptional environments and explain the pervasiveness of calcite buffering elsewhere. We argue that the injection of respired CO2into stagnant hydrological systems subject to evaporative discharge is the key context for high alkalinization. The evolution of evaporites in nature reaches highly alkaline stages only when excess of (bi)carbonate with respect to divalent cations occurs. In most dry landscapes, evaporating groundwater solutions lose this condition as respired inorganic carbon (recharge zone supply) equilibrates with divalent cations from rocks (whole hydro‐trajectory supply). Groundwater in stagnant landscapes avoids this limitation owing to short/shallow trajectories sustaining (bi)carbonate excess until evaporative discharge zones are reached. Flat sedimentary landscapes that are (i) wet enough to develop stagnation and have shallow water tables but (ii) sufficiently dry to expose them to evaporative concentration should host very alkaline soils. This is confirmed with >9,000 soil profiles from the global WISE database, which shows that profiles with pH ≥ 9 in the top meter are 2·7% globally but 18% in areas with low slope (<0·05%, 25‐km radius, SRTM digital elevation model (SRTM DEM)) and semiarid–subhumid climate (annual precipitation to potential evapotranspiration ratio = 0·2–1, CRU database). Understanding how climate and vegetation change as well as irrigation practices influence hydrological stagnation and evaporative concentration may provide the key to manage very alkaline environments. Copyright © 2017 John Wiley & Sons, Ltd.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".