Identification and Abundance of Barkley Canyon Megafauna: Daily Observations from 2012 to 2015 Using Ocean Network Canada Videos (BC, Canada)
Bibliographic record
Abstract
This dataset provides comprehensive records of species identified and their daily abundance in three deep-sites (Upper Slope -400m, Wall -900m, and Axis -1000m) of the Barkley Canyon (British Columbia, Canada) from 2012 to 2015. The data was collected through video recordings from the Ocean Network Canada observatory, which are publicly accessible using their SeaTube website. The dataset includes: - a detailed list of species = "species_list_match.csv" - a corresponding table of species names = "Species_details.csv" - daily abundance counts for each sites = "Abundance_SiteName.csv" - a file that outlines the methodology used for identification and counting = "ReadMe.txt" ---------- Method: - During each daily sampling, all 5-minute recordings made between 08:00 and 08:05 were viewed using VLC 2.0.1 © software to count and identify individuals at the lowest possible taxonomic level. All videos (2 minutes fixed + 3 minutes of scanning) were utilized. Videos were deemed unusable if viewing conditions were poor or particle counts were too high. When identification was not feasible, OTUs were defined. On the three sites mentioned, a camera mounted on a tripod recorded continuously at 5-minute intervals each day of the year. During these recordings, the background was illuminated using two spotlights. The camera recorded fixedly for the initial two minutes and could rotate from 0° (stationary, upper slope, pod2) to 360° (full rotation, canyon axis pod1, except between August 2014 and January 2015) and 180° (canyon wall, pod4). The illumination and zoom parameters, adjustable via the ONC site, were not consistent across all sites and sampling periods, affecting the observed surface area, ranging from 0.5 m² (upper slope, pod2 between May 2013 and May 2014) to a maximum of 9 m² (canyon axis, pod1 between mid-2013 and February 2014). The surface area was calculated each time the camera settings were changed (usually during annual maintenance missions) using a scaling grid developed from the camera’s two lasers, fixing a width of 10 cm on the ground as measured in the camera's field of view. Image captures were taken at intervals to cover the entire surface swept by the camera during rotation, estimating the sampled surface by overlaying the scaling grid on each image. - For more details on the methodology and to understand the context in which the data were collected (scientific questions, hypotheses, and studies conducted), you can refer to Pauline Chauvet's thesis (available in open access).
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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.004 |
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".