Kelp extent for the McNaughton Group Islands (2017), Manley Island (2017), and Serpent Group Islands (2016), British Columbia, Canada
Bibliographic record
Abstract
This is a polygon layer mapping kelp canopy extent for the McNaughton Group islands, Manley Island, and Serpent Group Islands. Kelp canopy extent was mapped from imagery taken using an unoccupied aerial system (UAS). McNaughton Imagery was taken in late May 2017, the Manley images were captured in late July 2017, and the Serpent Group images were made in early July 2016. The images were captured using a DJI Phantom 3 professional UAS equipped with a commercial grade 4k RGB (See attached UAS methods). Kelp bed polygons were drawn manually in ArcGIS 10.5.1. Only beds containing greater than ~3 fronds were mapped. Beds were considered connected when less than ~5m separated them. Single fronds and single frond outliers were not digitized. Vertices were placed every 4m or less (more vertices were used on complex shapes). The attributes in this table are limited to the perimeter length (m) and area (m^2) of each kelp bed polygon. Area was determined in the NAD_1983_CSRS_BC_Environment_Albers projection. Species and bed density were not recorded for this dataset.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".