Measuring Real Capital Input in OECD Agriculture
Bibliographic record
Abstract
This paper provides a farm sector comparison of levels of capital input for 17 OECD countries. The estimates of capital input are derived by representing capital stock as a weighted sum of past investments. The weights correspond to the relative efficiencies of capital goods of different ages, so that the weighted components of capital stock have the same efficiency. We convert estimates of capital stock into estimates of capital services by means of capital rental prices. Comparisons of levels of capital input among countries require data on relative prices of capital input. We obtain relative price levels for capital input among countries via relative investment goods prices, taking into account the flow of capital input per unit of capital stock in each country. Cet article se propose de comparer le niveau du capital dans Vagriculture de 17pays de l'OCDE. Les estimations du niveau de capital sont déduites en calculant un stock de capital comme une somme pondérée des investissements passés. Les poids utilisés correspondent a Vefficacite relative des biens capitaux à différents âges, de telle sorte que les différentes composantes du stock du capital ait la même efficacité. Le stock de capital est déduit une estimation des services de capital en utilisant un taux de rentabilité. Cette comparaison des niveaux de capital entre pays nécessite des données sur les prix relatifs du capital. Ces prix dépendent des prix relatifs des biens capitaux mais aussi des flux de service générés dans chaque pays par une unité de capital.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".