Annual catalogue of harvesting machinery.
Bibliographic record
Abstract
Reducing FrictionRoller and Ball Bearings.-Wewere the first amonff Canadian manufacturers to introduce the feature of Roller and Ball Bearings in a new Mower, properly designed so as to embody this valuable improvement, though some other manufacturers have endeavored to remodel their old mowers for the purpose of inserting Roller Bearings.Those who have not adopted the improvement will, doubtless, continue to say that Roller Bearings are no good, but the fact is, this form of Bearing has come to stay.We use these Roller and Ball Bearings to prevent the waste- ful friction of mechanism, which is done by changing the sliding Arrangement of Roller and Ball Bearings in Frame of No. 8 contact between the Axle or Shaft and the Journal Box in which it turns, to rolling contact.Ball Bearings have for manyyears been used with perfect satisfaction in many varieties of machinery, with a great saving of power, besides a saving of wear on the machine ; but, in machinery where there is any considerable weight or strain to be borne, as in heavy shafting, cars, etc., Roller Bearings are used in place of Ball bearings, and they have now been long enough in use to demonstrate their practical value.All farmers will quickly recognize the great value of Roller Bearings in Harvesting Machinery, where the power is limited and a saving of draft means a saving of horse flesh and of wear Rollers in Frame ^^^^^machine parts, and we have made use of them in our No. 8 Mower to the fullest extent that it is possible to
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.430 | 0.456 |
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".