The Global Soundscapes Project: overview of datasets and meta-data
Bibliographic record
Abstract
This is an overview of the soundscape recording datasets that have been contributed to the Global Soundscapes Project, as well as associated meta-data. The audio recording criteria justifying inclusion into the current meta-dataset are: Stationary (no towed sensors or microphones mounted on cars) Passive (no human disturbance by the recordist) Ambient (no focus on a particular species or direction) Recorded over multiple sites of a region and/or days The individual columns are described as follows. General: ID: primary key name: name of the dataset subset: incremental integer that can be used to distinguish sub-datasets collaborators: full names of people deemed responsible for the dataset, separated by commas date_added: when the dataset was added Space: realm_IUCN: realm from IUCN Global Ecosystem Typology (v2.0) (https://global-ecosystems.org/) medium: the physical medium the microphone is situated in GADM0: for terrestrial locations, Database of Global Administrative Areas level 0 unit as per https://gadm.org/ GADM1: for terrestrial locations, Database of Global Administrative Areas level 1 unit as per https://gadm.org/ GADM2: for terrestrial locations, Database of Global Administrative Areas level 2 unit as per https://gadm.org/ IHO: International Hydrographic Organisation sea area as per https://iho.int/ latitude_numeric_region: study region approximate centroid latitude in WGS84 decimal degrees longitude_numeric_region: study region approximate centroid longitude in WGS84 decimal degrees topography_min_m: minimum elevation of sites from sea level topography_max_m: maximum elevation of sites from sea level ground_distance_m: vertical distance of microphone from land ground or ocean floor freshwater_depth_m: vertical distance from water surface for freshwater datasets sites_number: number of sites sampled Time: days_number_per_site: typical number of days sampled per site (or minimum if too variable) day: whether the sites were sampled during daytime night: whether the sites were sampled during nighttime twilight: whether the sites were sampled during twilight warm_season: whether the warm season was sampled. Only outside tropics (https://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification) cold_season: whether the cold season was sampled. Only outside tropics (https://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification) dry_season: whether the dry season was sampled. Only for tropics (https://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification) wet_season: whether the wet season was sampled. Only for tropics (https://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification) year_start: starting year of the sampling year_end: ending year of the sampling schedule: description of the sampling schedule, free text recording_selection: criteria used to temporally select recordings (e.g., discarded rainy days) Audio: high_pass_filter_Hz: lower frequency of the high-pass filter sampling_frequency_kHz: frequency the microphone was sampled at audio_bit_depth: bit depth used for encoding audio recorder_model: recorder model used microphone: microphone used recordist_position: position of the recordist relative to the microphone during sampling Others: comments: free-text field URL_project: internet link for further information URL_publication: internet link of the corresponding publication More information on the project can be found here: https://ecosound-web.uni-goettingen.de/ecosound_web/project/gsp
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 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; both teacher heads agree on what is shown here.
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".