Bibliographic record
Abstract
in the act of radiating off, like the spokes of a wheel, from a centre in the distance, as the spectator is borne swiftly past them in the train.Countless fields, all smooth and clean : here, grass and meadow ; there, wheat, rye, the stately maize, and cereals of every name ; with pulse, roots, gourds, esculents of every form ; acres of garden ; acres of nursery ground ; acres of apple- orchard ; in favoured regions, acres of peach-orchard and acres of vineyard; acres of enclosures for the lesser fruits -the numerous summer or winter berries.And in keeping with these scenes of plenty and advancement, there are the solid homestead dwellings distributed plentifully about, almost everywhere now in view of each other; each with its roomy surroundings of spacious sheds, granaries, stabling, and cattle-housings ; and often its tasteful pleasure-grounds, its tree-shadowed avenue of approach, its handsome entrance-gates.Add vehicles for locomotion, cleverly adapted to their several purposes ; and public highways, broad and well-kept, graced here and there with a survivor of the primitive wood, less frequently, perhaps, than one might desire, assuming now grand dimensions and a picturesque venerableness.What are all these things but so many reproductions of, and in some respects improvements on, the old mother-land, only under a sky more cloudless, amidst an air more transparent ?
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".