Geologic Controls on Apparent Root‐Zone Storage Capacity
Bibliographic record
Abstract
Abstract The water storage capacity of the root zone can determine whether plants survive dry periods and control the partitioning of precipitation into streamflow and evapotranspiration. It is currently thought that top‐down, climatic factors are the primary control on this capacity via their interaction with plant rooting adaptations. However, it remains unclear to what extent bottom‐up, geologic factors can provide an additional constraint on storage capacity. Here we use a machine learning approach to identify regions with lower than climatically expected apparent storage capacity. We find that in seasonally dry California these regions overlap with particular geologic substrates. We hypothesize that these patterns reflect diverse mechanisms by which substrate can limit storage capacity, and highlight case studies consistent with limited weathered bedrock extent (melange in the Northern Coast Range), toxicity (ultramafic substrates in the Klamath‐Siskiyou region), nutrient limitation (phosphorus‐poor plutons in the southern Sierra Nevada), and low porosity capable of retaining water (volcanic formations in the southern Cascades). The observation that at regional scales climate alone does not “size” the root zone has implications for the parameterization of storage capacity in models of plant dynamics (and the interrelated carbon and water cycles), and also underscores the importance of geology in considerations of climate‐change induced biome migration and habitat suitability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".