Mapping Canopy-Forming Kelps in the Northeast Pacific: A Guidebook for Decision-Makers and Practitioners
Bibliographic record
Abstract
This guidebook provides an overview of optical remote sensing as it relates to mapping giant kelp and bull kelp. We describe optical remote sensing platforms and sensors pertinent to mapping and monitoring attributes of bull kelp and giant kelp beds - kelp presence/absence, density, species, and health. Recommendations found in this guidebook can also reasonably be applied to other floating, emergent canopy-forming kelp species (i.e. other kelp that float at the ocean’s surface). This guidebook provides monitoring guidance via infographics developed by an international community of kelp remote sensing experts. We illustrate a user-friendly framework based on the latest remote sensing science for matching a kelp monitoring objective(s) to the desired spatial scale and environmental setting. The goal of this guidebook is to help you, our reader, select the best remote sensing tools and data for your science and/or management directives related to kelp mapping. The guidebook is available in both English and Spanish.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.104 |
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".