Southern California Vegetation, Wildfire, and Erosion Had Nonlinear Responses to Climatic Forcing During Marine Isotope Stages 5–2 (120–15 ka)
Bibliographic record
Abstract
Abstract A multiproxy record from Baldwin Lake, San Bernardino Mountains, allowed us to examine variation and relationships between erosion, wildfire, vegetation, and climate in subalpine Southern California from 120 to 15 ka. Bulk organics, biogenic silica, and molar C:N data were generally antiphased with magnetic and trace element data and displayed long‐term (105 year) shifts between autochthonous and allocthonous deposition. This was most pronounced during Marine Isotope Stage (MIS) 5, and we hypothesize that local summer insolation was the primary driver for Baldwin Lake's productive and unproductive lake state alternations. Wildfire history was inferred from charcoal concentrations and vegetation change from pollen. Relationships between these ecological processes, basin deposition, and summer insolation were often nonlinear. Sagebrush expansion, wildfire, and weak basin weathering characterized MIS 4, while during MIS 2, the basin was highly erosive, rarely burned, and the forest was impacted by shifts in Southern Californian hydroclimate. Despite coniferous forest cover throughout MIS 3, submillennial oscillations in charcoal, pollen, and bulk organic content occurred, consistent with pollen records from Eurasia's Mediterranean biome that span multiple glacial‐interglacial cycles. Highly resolved global CO2 records and sea surface temperatures in key regions of the Pacific show no apparent relationship to these landscape conditions, and we suggest submillennial hydroclimatic variability as a potential driver. Highly resolved long pollen records from Southern California are an urgent research need to better understand the finer‐scale (≤103 year) interactions between past vegetation, wildfire, and erosion, given the current natural disaster risks that 21st century climate change poses to both human and ecological communities.
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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 | 0.000 |
| 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".