Bibliographic record
Abstract
As a curtain of trees opens and dawn breaks over the glistening waters of Lake Ontario, a fish and a bird spot each other through the surface and fall madly in love. They dance and sing together from their respective atmospheres, yet each time the bird soars past the water's edge and onto the beach, the fish cannot follow. Night after night, by the light of the moon, the fish trains herself to leap out of the water, hoping to breach high enough to join her beau above. Late one afternoon, the fish approaches the shore, where the bird stands with his flock. She launches herself from the water, and as she sparkles in the sun, the two creatures see each other plainly for the first time. She's flying, she's falling–and she's flying again as the bird hoists her up, up, up by the tail. The fish resumes their song alone as she tastes the clouds for the first time. The bird drops her from a great height and she lands, broken, in his nest. The fish gasps her final notes, drying out while the sun sets on the lake. The bird lands over her, silhouetted by the setting sun, and feasts on her remains. A Fish & A Bird spawned from the heart-wrenching feelings of an ill-fitting relationship, which can be viscerally gutting, indulgently overdramatic, and a little ridiculous. The film that emerged juggles all three, drawing audiences in with the absurd humor of realistic creatures performing a full-throated opera, letting them believe in the star-crossed love, and ripping it away in a genre-fitting conclusion. The film strives to both poke fun at and harness the strengths of opera and animation while telling a sweeping tale of an unlikely pairing.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".