Mapping Canopy-Forming Kelps in the Northeast Pacific: A Guidebook for Decision-Makers and Practitioners
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
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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