Bibliographic record
Abstract
Gwyneira – l’une des deux heroines du Pays du nuage blanc – eleve sa petite-fille Kura et s’inquiete pour elle. La jeune metis de quinze ans, surnommee la reine maorie, possede un physique et une voix magnifiques qui suscitent l’admiration. Mais, capricieuse et uniquement interessee par la musique, Kura refuse d’assumer son role d’heritiere de Kiward Station, le domaine familial. Gwyneira decide alors de l’envoyer en pensionnat pour lui donner une education stricte. C’est alors que Kura rencontre William Martyn – le fiance de sa cousine, la petite-fille d’Helene – autre heroine du Pays du nuage blanc… Le coup de foudre est reciproque. Kura et William decident de se marier, au grand dam de tous. Kura pourra-t-elle cependant se contenter d’un role d’epouse alors qu’une grande carriere musicale lui est promise ? Portee par un puissant souffle romanesque, ce roman confirme le talent d’une auteure decouverte avec Le Pays du nuage blanc…
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".