An Investigation on Sieve and Detour Effects Affecting the Interaction of Collimated and Diffuse Infrared Radiation (750 to 2500 nm) With Plant Leaves
Bibliographic record
Abstract
The retrieval of plant biophysical and biochemical properties from high spectral resolution data represents an active area of research within the remote sensing field. Scientific studies in this area are usually supported by computational simulations of light attenuation processes within foliar tissues. In heterogeneous organic materials, like plant leaves, sieve and detour effects can affect these processes and ultimately change the light gradients within these tissues and their spectral signatures. Although these effects have been extensively examined for applications involving the interactions of visible radiation with plant leaves, little is known about their role in the infrared domain. In this paper, we describe the procedural basis for their incorporation in the modeling of infrared-radiation transport (in the range of 750-2500 nm) within plant leaves. We also assess their impact on the predictability of simulation solutions relating the directionality of the incident radiation and the internal arrangement of the tissues to changes on foliar spectral signatures in this domain. Our investigation is grounded by the observations involving the modeled results and quantitative and qualitative data reported in the literature.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".