Biases in LI-COR Plant Canopy Analyzer estimates of seasonal light interception by black spruce and trembling aspen canopies
Bibliographic record
Abstract
This article examines four sources of bias in the estimation of seasonal light interception by black spruce (Picea mariana (Mill.) BSP) and trembling aspen (Populus tremuloides Michx.) canopies using the LI-COR LAI-2000 Plant Canopy Analyzer: (i) geometric averaging of gap fractions, (ii) unrepresentative elevation angle intervals for radiation detectors, (iii) assumption of isotropic radiance distribution, and (iv) inclusion of light intercepted by stems in the estimate of canopy light interception. Bias source i caused overestimates of canopy light interception, source ii caused underestimates, source iii caused little bias, and source iv caused overestimates. The magnitude of bias from sources i, ii, and iv increased as fractional light interception by canopies decreased. The average bias in seasonal light interception for all sources combined was 5% for black spruce canopies and 17% for trembling aspen canopies. It is recommended that canopy light interception and understorey light level estimates be calculated in a way to reduce bias from sources i and ii. For estimates of canopy interception alone, bias from source iv should also be reduced.
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 | 0.003 | 0.006 |
| 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 | 0.001 | 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".