Improving gap light index (GLI) to quickly calculate gap coordinates
Bibliographic record
Abstract
Understory light is essential to the establishment and growth of understory plants and varies temporally and spatially within gaps. The previously defined gap light index (GLI) is a good model for assessing understory light levels, but it is time-consuming to determine gap coordinates, which are crucial to GLI, for numerous points within a gap. This paper introduces the geometric calculation (GeoCalc) of gap coordinates. GeoCalc quickly obtains gap coordinates for any specified point within a canopy gap and takes into account the tridimensional profile of the gap and the slope and aspect of the ground. The GeoCalc-based GLI was validated by the GLI derived from hemispherical photographs taken at 93 sampling points within seven natural gaps. Our results demonstrate that GeoCalc-based GLI was strongly positively correlated and not significantly differed from the GLI derived from hemispherical photographs. Next, to analyze gap light regimes and the effects of gap size, canopy height, and topography, three natural gaps of various size were selected and simulated as nine gaps with 1 and 1.5 times canopy height or on the opposite slope. Finally, we have summarized characteristics of GeoCalc-based GLI and its application.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".