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Record W2932493797 · doi:10.1080/14942119.2019.1591074

The technical development of forwarders in Sweden between 1962 and 2012 and of sales between 1975 and 2017

2019· article· en· W2932493797 on OpenAlexaboutno aff
Tomas Nordfjell, Emil Öhman, Ola Lindroos, Bengt Ager

Bibliographic record

VenueInternational Journal of Forest Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsForwarderBusinessAgricultural economicsTractorAgricultural scienceForestryEngineeringOperations managementEconomicsGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Although being rather similar in appearance, forwarders have gone through substantial development during the more than half a century they have been used in forestry. The aim was to describe the development in Sweden, by a combination of historical narratives and data. The latter consists of technical parameters of forwarders sold on the Swedish market from 1962 to 2012, together with sales figures from 1975 to 2017. Data were collected from the original specifications from manufacturers; advertisements in old forestry magazines; Internet forums; and from the literature. In total, 51 forwarder manufacturing companies were identified, all located in Sweden or Finland, which produced over that time 361 models in total. The weight and load capacity has increased over time, as well as engine power and torque per tonne total weight. Load index has decreased over time. Ground pressure decreased from 1962 to 1985, but then remained stable. In Sweden, 12,602 forwarders were sold from 1975 to 2012. The trend for annual sales decreased until 1993 but since 2005 has increased to between 300 and 400 forwarders per year. Since 1995 annual sales in Sweden have been between four and six forwarders per million m3 harvested industrial wood. Corresponding values for the years 1975–1984 were double that. Why the first Swedish forwarder was developed from a farm tractor rather than importing an already existing, purpose-built forwarder from Canada is discussed, as well as the probable direction of future developments in forwarder design and construction. New forwarder size-classes are suggested. Soil damage issues are predicted to be of increased importance.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.012
GPT teacher head0.241
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2019
Admission routes1
Has abstractyes

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