A MISSÃO MILITAR FRANCESA E A LOGÍSTICA NO EXÉRCITO BRASILEIRO
Bibliographic record
Abstract
The French Military Mission (FMM) was a milestone in the evolution process of theBrazilian Army (BA). Every army is to be readiness to war. That is its destiny and, inorder to fulfill its mission, it needs trained men and modern material. The FMM’s taskwas to instruct the Brazilian military with what was most advanced in that periodafter the First World War, so that promote the reorganization of the BA, giving it a reavaliable degree of operationality, and for this the work of logistics is essential.FMM was contracted in 1919 and extended until 1940. Discussions, in the politicaland military circles, for the contracting of a military instruction mission were intense,from the question of which country should be contracted, to the constitutionalityof the contracting, arriving at economic-commercial issues for the acquisitionon military material and equipment. At the end of the mission, BA presents a newstructure in its logistics. This paper aims to analyse the French Military Mission inBrazil and its importance for logistics in the BA. As a result, it was fundamental for themodernization of the means and structures of the BA, which were materialized by theacquisition of new equipment and armaments, and by the development of factoriesand arsenals. With this restructuring, it became essential to create the Quarter Corpsin the composition of the Land Force, along the lines of European armies, initiating thesystematic development and work of logistics in the Brazilian Army.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".