Linking LiDAR with streamwater biogeochemistry in coastal temperate rainforest watersheds
Bibliographic record
Abstract
The goal of this study was to use watershed characteristics derived from light detection and ranging (LiDAR) data to predict stream biogeochemistry in Perhumid Coastal Temperate Rainforest (PCTR) watersheds. Over a 2-day period, we sampled 37 streams for concentrations of dissolved C, N, P, major cations, and measures of dissolved organic matter quality (specific ultraviolet absorbance, SUVA254) and bioavailability. Random forest – classification and regression tree analysis showed that aboveground biomass and structure and watershed characteristics, inclusive of mean watershed slope and elevation, watershed size, and topographic wetness, explained more than 60% of the variation in concentration for most measured constituents. These results indicate this approach may be particularly useful for predicting stream biogeochemistry in small forested watersheds where fine resolution is needed to resolve subtle differences in forest biomass, structure, and topography. Overall, we suggest that the use of LiDAR in many of the small and remote watersheds across the Southeast Alaskan PCTR as well as other forested regions could help inform land management decisions that have the potential to alter ecosystems services related to watershed biogeochemical fluxes.
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 | 0.001 | 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".