Mapping the distribution of an invasive marine alga (<i>Codium fragile</i>spp.<i>tomentosoides</i>) in optically shallow coastal waters using the compact airborne spectrographic imager (CASI)
Bibliographic record
Abstract
We collected 19 bands of ocean colour data (spanning 391–904 nm) at 1 m2 spatial resolution from Mahone Bay on the south coast of Nova Scotia during July 2001 with an airborne hyperspectral sensor (CASI). The data were classified using 16 different protocols in an effort to accurately map benthic communities over 7 km2 of seabed to 6 m water depth. The primary objective was to depict the spatial pattern of invasion of the rocky subtidal zone by an introduced macroalga: Codium fragile. The best classification results were obtained using the first three axes of a principal component analysis of all 19 spectral channels and the maximum likelihood classification of four classes of benthic community (i.e., those dominated by Codium meadow, kelp bed, Codium–kelp mix, and sand) at three depth strata using an added bathymetry channel and no correction for water-column attenuation. The overall accuracy obtained for the entire visible seabed of the bay was 83.30% (Kappa statistic = 0.82). User's and producer's accuracies of the four classes ranged from 35.00% to 97.00% and 65.00% to 100.00%, respectively, depending primarily on depth. The addition of a bathymetry channel typically increased the overall accuracy by 20.00%, and a correction for water-column attenuation had little effect at these depths. Patches of the invasive alga, kelp, and sand were clearly distinguishable at spatial scales of 1–1000 m2, but there was also patchiness at subpixel scales (i.e., <1 m2), such that Codium was frequently confused with mixed communities. We interpret this multiscale patchiness as indicative of ongoing invasion dynamics and conclude that airborne hyperspectral technology is suitable for portraying the time-dependent outcomes of these dynamics at ecologically meaningful spatial scales.
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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 | 0.001 | 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) | 0.000 | 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".