Assessing decadal soil redistribution rates using <sup>239+240</sup> Pu across diverse lithologies in Southeast Alaska
Bibliographic record
Abstract
Abstract Quantifying soil redistribution rates, including both erosion and deposition, is critical for understanding erosion processes, landscape evolution, land management strategies, and the carbon cycle. In the Northeast Pacific coastal temperate rainforest, the interaction of perhumid climate and dense coniferous forest tends to form Spodosols which are soils characterized by a subsurface accumulation of organic matter and iron and aluminum oxides, across a range of contrasting lithologies. Deep Spodosols are frequently found on steep backslopes (up to 60%) of colluvial deposits, where shallower soils would typically be expected. We hypothesized that deep Spodosols in Southeast Alaska indicate slope stability, exhibiting negligible soil redistribution rates and stable surfaces regardless of the lithology. Our objective was to quantify soil redistribution rates for Spodosols formed on steep slopes across a range of lithologies in hilly and mountainous areas of Juneau, AK. We used 239+240 Pu isotopes to quantify soil erosion and deposition rates in Spodosols formed on colluvial deposits from tonalite, slate, metavolcanic rock, and phyllite. 239+240 Pu measurements revealed negligible soil redistribution rates for all studied pedons, ranging from erosion rates of 0.51 t/ha/year to deposition rates up to 0.43 t/ha/year. No difference was detected between the hill and mountain landforms, further supporting the idea that Spodosols could indicate slope stability over decadal timescales across the region. Understanding the resilience of Spodosols to erosion processes in varied lithologies and landforms on steep slopes is paramount for making informed decisions regarding sustainable land use, landslide risk mitigation, and effective carbon sequestration strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".