Bibliographic record
Abstract
At night we swim / following the fence: / diverted / we enter the net / shaped like a heart / and in the heart the hook / guides us to the back A stunning unfolding of memory, Wavelengths of Your Song juxtaposes a childhood in the northern Canadian wilderness with the adventures of an international creative life. Genuine environmentalism is at the heart of this collection. Migrations of birds and humans lend their songs to the vivid writing and a tangible, sensory reality emerges from their sounds. Music by Beethoven and Rzewski, paintings by Norval Morrisseau and Kandinsky, and writing by Kafka and Celan, inspire Eleonore Schönmaier's poetry. She takes the reader on unexpected journeys skiing across frozen lakes, cycling along Dutch canals, or hiking in Malta and New Zealand. With surprising, at times breathtaking connections, she illuminates hot air ballooning, canoe camping, planting trees on Vienna rooftops, and the bathing of a black horse in the North Sea. In poems that travel extensively around the globe, in lists for living well, and in love letters, Eleonore Schönmaier takes the reader on a journey along the wavelengths of the ocean, sound, and the physics of light.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.022 |
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".