Microphotometry of Underwater Shadowing by a Moss from a Niagara Escarpment Waterfall
Bibliographic record
Abstract
Microscope and fiber-optic spectrophotometry of transmittance and backscattering both showed moss leaves to be capable of casting strong shadows, with a single leaf blocking approximately 90% of incident light from a point source. In leaves with only one layer of cells, the transmittance through the cytoplasm of single cells was similar to that for whole leaves. Analysis of cell wall birefringence by polarized-light interferometry indicated that cell walls might normally scatter rather than transmit light. Spectra transmitted through, or backscattered from, the upper green layers of moss were dominated by selective absorbance from chlorophyll, but there was also evidence of wavelength-dependent scattering, as detected in the lower layers of brown, dead moss. Specular reflectance from moss leaves was detected by polarimetry and may have contributed to the relatively high macroscopic transmittance of stationary moss in water. Shadowing by moss leaves was confirmed by dynamic measurements of mosses in turbulent water without bubbles. Flicker patterns from leaves were superimposed on the underwater flicker pattern created at the air-water interface, thus flecks of light were reduced in intensity, increased in frequency, and decreased in duration. This was detected with both point source and diffuse illumination of samples.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".