Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest
Bibliographic record
Abstract
Abstract Questions Can the ratio of nitrogen to phosphorus (N:P ratio) be predicted at canopy level using imaging spectroscopy (IS) and light detection and ranging (LiDAR) remote sensing data? How do temporal variation and difference in spatial resolution of these data sources affect prediction accuracy of the canopy N:P ratio? Location Boreal mixedwood forest, northern Ontario, Canada. Methods Canopy N:P ratio was estimated using spectral indices calculated from IS data at two spatial resolutions, airborne and space‐borne, across two summers. The relationship between the canopy N:P ratio and forest structure was investigated through analysis of LiDAR data. The impact of temporal variation on canopy N:P ratio and the different spatial resolution of IS data on prediction accuracy for canopy N:P was addressed. Maps of canopy N:P ratio generated from airborne and space‐borne IS data were generated. Results Airborne and space‐borne IS data explained 70% and 69% of the variation in canopy N:P, ratio, with predictions errors of 5.0% and 7.2%, respectively, in two consecutive years. Predictions differed significantly with changes in spatial resolution. Predictive models obtained from LiDAR data explained 54% and 67% of the variation in canopy N:P ratio, with prediction errors of 6.1% and 7.5%, respectively, for the 2 yrs. Conclusions The results show that canopy N:P ratio can be predicted with remote sensing data based on the relationship between canopy N:P ratio and crown closure at this site. The spatial variation due to the mixed deciduous and coniferous forest type is the underlying mechanism that generates the observed spatial pattern in canopy N:P ratio in this ecosystem, and the canopy N:P ratio map displays this variation.
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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 | 0.001 |
| 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 | 0.001 | 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".