Bibliographic record
Abstract
The overarching objective of our network is to bring together acousticians, cognitive psychologists, ecologists, and creative artists to integrate how they study and perceive soundscapes and use this knowledge to help shape a research agenda for the conservation of soundscapes. Many natural soundscapes are being threatened from various directions, e.g., habitat destruction, climate change, invasive species. This project aims at recording and documenting soundscapes in remote locations and identifying conceptualizations of these soundscapes across different cultures and disciplines. The network will help to (1) foster open communication between different disciplines and communities about soundscapes; (2) coordinate soundscape monitoring sites where acoustic data are being collected long-term; (3) develop a common vocabulary, long-term monitoring standards, and metadata standards for acoustic data for use by ecologists; (4) increase awareness of this new field among ecologists and social scientists; and (5) increase public awareness of the importance of their acoustic connection to nature. This project on the soundscapes of natural ecosystems is a logical complement to research underway at the European level on urban soundscapes (COST action TD0804). Together, this worldwide network of researchers will be uniquely situated to contribute decisively to a cross-cultural conservation framework for soundscapes.
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.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.016 |
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".