Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2009 & 2010 AUDIO DATA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Description of the data and file structureThis record contains all 2009 & 2010 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article: Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. Global Change Biology. In press. The data include the following: the 2009 & 2010 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13328931 - all 2011 & 2014 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu. Description of audio data filesThe data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags . Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them. Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software). We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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 it