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 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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.029 |
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".