Bibliographic record
Abstract
The 2018 California Current Ecosystem Survey (CCES) was a multidisciplinary survey of the marine ecosystem from southern British Columbia, Canada to northern Baja California, Mexico. CCES 2018 was conducted from 26 June to 4 December 2018 aboard the NOAA ship Reuben Lasker. In this report we present the preliminary results of the passive acoustic monitoring efforts using DASBRs. DASBRs were first used in a broad-scale Passive Acoustics Survey of Cetacean Abundance Levels (PASCAL) in the California Current during 2016, (Keating et al., 2018). Acoustic recordings were analyzed to detect echolocation signals from beaked whales, sperm whales (Physeter macrocephalus), and dwarf and pygmy sperm whales (Kogia spp.). In 2016, the most common beaked whale echolocation pulses were from Cuvier’s beaked whale (Ziphius cavirostris), Baird’s beaked whale (Berardius bairdii), Stejneger’s beaked whale (Mesoplodon stejnegeri), and two unidentified species of beaked whales whose echolocation pulses were referred to as BW43 and BW39V. Here we present analyses of the DASBR deployments from the CCES 2018 project.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.321 | 0.236 |
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".