Characterizing the light environment in Sierra Nevada mixed-conifer forests using a spatially explicit light model
Bibliographic record
Abstract
The spatially explicit light model tRAYci was calibrated to conditions in multi-aged Sierra Nevada mixed-conifer forests. To reflect conditions that are important to growth and regeneration of this forest type, we sampled a variety of managed mature stands with multiple canopy layers and cohorts. Calibration of the light model included determining leaf area density for individual species with the use of leaf area sapwood area prediction equations. Prediction equations differed between species and could be improved using site index. The light model predicted point measurements from hemispherical photographs well over a range of 27%63% light. Simplifying the crown representation in the tRAYci model to average values for species and canopy strata resulted in little reduction in model performance and makes the model more useful to applications with lower sampling intensity. Vertical light profiles in managed mixed-conifer stands could be divided into homogeneous, sigmiodal, and continuous gradients, depending on stand structure and foliage distribution. Concentration of leaf area in the upper canopy concentrates light resources on dominant trees in continuous canopies. Irregular canopies of multiaged stands, however, provide more light resources to mid-size trees and could support growth of shade-intolerant species. Knowledge of the vertical distribution of light intensity in connection with stand structural information can guide regulation of irregular stand structures to meet forest management objectives.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".