Vegetation quantification in a sub-arctic salt marsh using reflectance data
Bibliographic record
Abstract
Applications of remote sensing often require the derivation of a relationship between a desired variable, such as vegetation amount, and surface reflectance. Most frequently, linear regression analysis is used. For natural vegetation, relatively little attention has been paid to assessment of the statistical assumptions inherent in regression analysis, although a considerable effort has been made in developing radiometric indices. Using as an example a dataset collected at a long-term goose study site, a relationship between reflectance and aboveground biomass is established for a coastal salt marsh, a preferred foraging area for the geese. At this site, ground cover varies from a dense, short (less than 4 cm) cover of grasses and sedges to bare ground. Mosses and weedy species also occur. Reflectance data were collected for various cover types in the marsh in wavelength bands similar to the first five Landsat bands, at 0.5 m resolution using portable radiometric instruments. At each site, a subplot was sampled for biomass, leaf area index, and cover. Logistic tree regression was used to separate plots with zero vegetation. Linear regression, including data transformations and polynomial regression, was then explored. The vegetation indices tested produced broadly similar results. Band-wise regression generated equations which explained a larger proportion of variance than any index used alone, but vegetation indices with a quadratic term also provided a good fit. Model selection was based on minimizing the Akaike information criterion.
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 | 0.001 | 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 | 0.000 | 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".