Mapping shallow, benthic communities using hyperspectral, remotely sensed data: a test of habitat classification using multiple sources of bathymetry.
Bibliographic record
Abstract
ABSTRACT: Optical remote sensing technologies are increasingly used to map shallow, marine benthic communities. This approach largely relies on the use of bathymetric data, which vary greatly in accuracy and spatial resolution according to the source. We explored the effect of varying the quality of bathymetry on the output of classification maps of the invasive green alga Codium fragile ssp. tomentosoides along the Atlantic coast of Nova Scotia, Canada, using high resolution (1-m pixel) airborne hyperspectral imaging of a shallow ( 300 m). Our preliminary results indicate that the abundance of C. fragile derived using coarse resolution bathymetry can be up to 6 times greater than that based on a finer resolution. The omission of bathymetric data in an unsupervised classification (based on data streams retained by Principal Component Analysis) yielded only slightly higher estimates of abundance than classification using fine resolution bathymetry. These findings suggest a precautionary approach to the use of bathymetry in classification of shallow habitats in optically dense waters based on remotely-sensed data. This practice can result in substantial overestimation of the occurrence of specific habitat types or species, which may mislead those charged with the study and management of marine ecosystems and coastal resources.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".