Bibliographic record
Abstract
26 3-1 Recommended Daily Food Needs (1917) 34 10-1 Purchasing Power (Wholesale) of Agricultural Products 118 10-2 Indexes, Creamery Butter Production and Farm and Retail Prices 120 10-3 Profitability of Various Uses of Raw Milk 120 10-4 Farms Reporting Milk Cows, 1961-71 121 10-5 Per-Capita Weekly Food Purchases by Family Income Quintile Group, Canada, 1969 and 1974 125 10-6 Major Margarine Developments, 1890-1967 126 10-7 Land Seeded to Domestic Oil Seeds 128 10-8 Percent Distribution of Oils Used in Margarines in Canada 129 10-9 Comparative Economic Efficiency 129 10-10 Census Farms Reporting Milk Cows 131 10-11 Farm Data by Total Capital Value -Canada 132 10-12 Quarterly Wholesale Butter Prices in Canada and in Selected Countries, 1958 133 11-1 Nutritive Value, Edible Portion of lOOg (1980) 136 11-2 Actual Contribution to Fat by Major Foods in Canada, 1960-75 139 11-3 Percentage Contribution to Total Fat by Major Foods in Canada, 1960-75 140 11-4 Per-Capita Annual Supplies of Food Moving into Consumption 141 11-5 Percent Distribution of Margarine Samples Containing Less than 5% to More than 20% Cis, Cis, Methylene-interrupted Polyunsaturated Fatty Acids (CCM1) 142 12-1 Average Retail Price Per Pound 150 v
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.755 | 0.578 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".