Sensitivity and Correlation Analysis of PROSPECT-D and ABM-B Leaf Models
Bibliographic record
Abstract
Two leaf optical property models, PROSPECT-D and ABM-B, were compared to determine their respective parameter sensitivities and to correlate their parameters. ABM-B was used to generate 150 leaf spectra with various input parameters, and the inversion of PROSPECT-D was used to estimate leaf parameters from these spectra. Wavelength-specific sensitivities were described, and correlations were developed between the leaf pigments and structure parameters of the two models. Of particular importance was the correlation of PROSPECTD's structure parameter (N) which is a generalized parameter integrating several leaf-level and cell-level characteristics. At the leaf-level, N showed correlations with the leaf thickness and the mesophyll percentage, and at the cell-level, N was affected by the cell cap aspect ratios defined in ABM-B. The estimated value of N also varied substantially with changes in the angle of incidence specified in ABM-B. All of these correlations were nonlinear, and it is unclear how these parameters are combined to affect the final value for N. The correlations developed in this article indicate that additional structural parameters (possibly separated into leaf-level and cell-level) should be considered in future model development that aims to maintain inversion potential while providing more information about the leaf.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".