Bibliographic record
Abstract
My journey hitherward by a morning's sail from Toronto across Lake Ontario, seemed to me, as regards a certain dull vacuity in this episode of travel, a kind of calculated preparation for the uproar of Niagara—a pause or hush on the threshold of a great impression; and this, too, in spite of the reverent attention I was mindful to bestow on the first seen, in my experience, of the great lakes. It has the merit, from the shore, of producing a slight ambiguity of vision. It is the sea, and yet just not the sea. The huge expanse, the landless line of the horizon, suggest the ocean ; while an indefinable shortness of pulse, a kind of fresh–water gentleness of tone, seem to contradict the idea. What meets the eye is on the scale of the ocean, but you feel somehow that the lake is a thing of smaller spirit. Lake–navigation, therefore, seems to me not especially entertaining. The scene tends to offer, as one may say, a sort of marine–effect missed. It has the blankness and vacancy of the sea, without that vast essential swell which, amid the belting brine, so often saves the situation to the eye. I was occupied, as we crossed, in wondering whether this dull reduction of the main contained that which could properly be termed “scenery.” At the mouth of the Niagara Eiver, however, after a sail of three hours, scenery really begins, and very soon crowds upon you in force. The steamer puts into the narrow channel of the stream, and heads upward between high embankments.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".