Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Forwarder fuel consumption was studied by examining a total of 27 forwarders under field conditions. Three datasets, representing different data acquisition methods, were used. In a field study, time and fuel consumption by work-element of two 20–21 tonne forwarders in final felling were recorded. In a questionnaire survey, daily data concerning fuel consumption, productivity and average extraction distance was provided on 18 forwarders, divided between final felling and thinning. Finally, accounting data on fuel consumption for 11 forwarders were obtained. In the field study, the fuel consumption varied between 8.3 to 15.7 l/PMH (productive machine hour) for different work elements. The total fuel consumption was 0.28–0.36 l/m3sub (solid under bark) at average extraction distances on 360–412 m for loads of sawlogs and 0.43–0.66 l/m3sub (458–514 m) for loads of pulpwood. 61–62% of that fuel was consumed during loading and driving during loading. The forwarders consumed 0.23–0.38 l/100 m driving and the difference was only 10% with and without load. In the questionnaire survey, the fuel consumption averaged 0.62 l/m3sub (sawlogs and pulpwood, 318 m average extraction distance) for final felling (16–20 tonne forwarders) and 0.92 l/m3sub (644 m) for thinning (11–14 tonnes). An exception was 2.5 tonne forwarders that consumed only 0.35–0.37 l/m3sub (120–180 m). 89% of the extracted volume in the accounting data was from thinnings and the fuel consumption was in average 0.67 l/m3sub (100–200 m) for 9 to11 tonne forwarders. More difficult terrain conditions, the use of tracks and wheel-chains and one more assortment in the questionnaire survey are the most probable reasons for higher fuel consumption than in the field study. At long extraction distances it is especially important to utilize the maximum load capacity to benefit low fuel consumption on m3 basis.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it