The combined use of visual and acoustic data collection techniques for winter killer whale ( Orcinus orca ) observations
Bibliographic record
Abstract
Observations of cetaceans during the winter are difficult, if not impossible in some locations, yet their presence, habitat use, and behaviour during this period are important for conservation and management. Typically, observations come from vessel surveys, with citizen science networks increasingly adding significant sighting data. In compliment to this, acoustic data collection systems can be deployed to collect information remotely over long periods, and in almost any conditions. Here we describe how the combination of these data collection techniques works to fill knowledge gaps, with data from a well-established citizen science network, and a single passive acoustic monitoring (PAM) recorder integrated to identify killer whale presence during winter months in Clayoquot Sound, on the west coast of Vancouver Island. Together these data show the overwinter use of Clayoquot Sound by killer whales is greater than previously thought. During the study period, February 21 to April 25, 2015, the citizen science network noted 14 visual encounters ranging from Amphitrite Point to Hot Spring Cove, Vancouver Island. The PAM recorded 17 acoustic encounters within the 10 km detection radius of the recorder, deployed off Siwash Point, Flores Island. This included 15 encounters not recorded by the visual network. Both resident and Bigg’s (transient) transient whale groups were recorded, although analysis of vocalizations determined that the majority of the encounters recorded acoustically were of northern resident killer whales. This may be a function of life history, with Bigg’s killer whales typically noted to be less acoustically active, or could represent greater site use by this group. This first use of acoustic monitoring over the winter, complemented with visual data, can establish a better understanding of year-round use of this area by killer whales and has broader application to other sites.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".