Spatial extent of eelgrass (Zostera marina) beds from monitoring sites within the greater park ecosystem of Pacific Rim National Park Reserve (2017, 2018)
Bibliographic record
Abstract
This data package contains reports and datasets pertaining to mapping the spatial extent of eelgrass (Zostera marina) at several monitoring sites within the greater park ecosystem of Pacific Rim National Park Reserve located on the west coast of Vancouver Island, British Columbia, Canada. This work was a collaborative project between the Hakai Institute and the Pacific Rim National Park Reserve (PRNPR). Monitoring sites were mapped using a small Remotely Piloted Aerial Systems (RPAS) (DJI Phantom Pro 3) during summer low tides in 2017 and 2018. Orthomosaics of each site were created using a Structure from Motion Multi-View Stereo (SfM-MVS) workflow within Pix4Dmapper software (Version 2.1.61, Pix4D) in Windows 10. For the 2017 survey, ground control points were collected for georeferencing the orthomosaics. This was not done in 2018 due to time limitations (please see reports for more details). The extent of eelgrass at each site was delineated using object-based image analysis (OBIA) with eCognition Developer software (eCognition Developer 9, 2014) and manual delineation (where necessary). Georeferenced towed underwater video (SplashCam Pro) data were collected at eelgrass monitoring sites during high tide to provide ground-truth data for the aerial analysis and delineation of eelgrass. The data package includes: Temporal coverage: July 2017 (Broken Group Unit region) and May 2018 (Long Beach Unit region) This data package is a component of the Hakai Institute’s Habitat Mapping program. The overarching objective of the Hakai Habitat Mapping program is to generate spatial inventories of coastal habitats, investigate how these habitats are changing through time and the drivers of that change. The use of this dataset requires permission from both the PRNPR and the Hakai Institute. To see a copy of this data sharing permissions, see LICENCE.txt. Please attribute material in this data package as: Reshitnyk, L. Y, and J. Yakimishyn. (2021). Spatial extent of eelgrass (Zostera marina) beds from monitoring sites within the greater park ecosystem of Pacific Rim National Park Reserve (2017, 2018). Version 1.0. Hakai Institute. Dataset. [access date]) DOI: https://doi.org/10.21966/8mpe-h081
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".