Passive remote sensing technology for mapping bull kelp (Nereocystis luetkeana): A review of techniques and regional case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The distribution and abundance of the canopy-forming kelp Nereocystis luetkeana is of increasing concern for environmental management and conservation in coastal regions due to its importance as a foundation species. Mapping kelp forests aids in understanding their health, productivity, and response to environmental conditions. Remote sensing using satellites is an increasingly accessible tool for mapping nearshore habitats allowing for applications such as long-term monitoring and large- and small-scale surveys. This paper provides a review of passive optical remote sensing techniques for detection and mapping of floating macro-algae, and adapts these techniques for detecting Nereocystis luetkeana, demonstrating their application through a comprehensive case study, from imagery acquisition to map validation. This review with associated case study communicates to non-remote sensing experts a road map to use remote sensing technology for mapping kelp habitats.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it