Attenuation of cosmic ray flux in temperate forest
Bibliographic record
Abstract
Forests alter secondary cosmic radiation (CR) to the ground by (1) diminishing it owing to absorption by trees, (2) inducing spatial and temporal variability because biomass distribution is heterogeneous, and (3) lengthening the apparent mean attenuation length at ground level because nucleons are shielded over muons. We model CR flux through three‐dimensional simulated forests with properties drawn from old‐growth plots in Nova Scotia, Canada (Acadian forest) and the Olympic Peninsula, Washington state (coastal rain forest). For exposure durations of ≥105, conservative mean shielding in rain forest is 7.3 ± 2.3%, canopy and floor biomass included. Acadian/boreal forest has mean shielding 2.3 ± 0.6%. These long‐timescale mean values are similar to previous estimates from treating forest biomass as a layer of constant thickness. Ground flux varies significantly between sites within a forest, ranging from 1 to 100% of nonforested flux for short timescales if some trees are large (diameters ≥1.5 m) because the position of the sample site relative to individual large trees is important. Temperate rain forests have large trees and disturbance/regeneration intervals approaching 103 y; hence, CR flux and resultant terrestrial cosmogenic nuclide (TCN) concentrations vary by 1.5% after 8000 y but only 0.2% after 80,000 y. These results are for a forest that is statistically uniform through time; changes in biomass heterogeneity through time and/or space, owing to climate, wind‐throw, or localized recruitment, would increase the inherent variability of TCN production. TCN dating experiments on timescales much shorter than 80 secondary successions of the forest will have significant uncertainty in effective production rates but catchment‐wide average erosion experiments will not.
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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".