CentipedeRTK, un réseau pour la géolocalisation haute précision au service de l'environnement
Bibliographic record
Abstract
Positioning RTK, or Real-Time Kinematics, is a long-established technology that improves the positioning provided by a GNSS mobile receiver, based on a network of fixed reference receiver antenna, precisely positioned over the territory. Although highly effective, this solution is still very costly and therefore not available to all users. The emergence of low-cost electronic products has given rise to the CentipedeRTK network. The network is based on an open and shared methodology for building RTK antennas that are networked for free and collaborative use, whatever their purpose. Since its launch in 2019, the network has gone from strength to strength, and its uses are multiplying in a variety of fields, from forestry research to environmental monitoring. Le positionnement RTK ou Cinématique Temps réel est une technologie éprouvée qui permet d’améliorer le positionnement fourni par un récepteur mobile GNSS, en se basant sur un réseau de récepteurs à antenne, fixes, servant de référence, positionnés précisément sur le territoire. Bien que très efficace, cette solution reste encore très coûteuse et n’est donc pas à la portée de tous les utilisateurs. L’émergence des produits électroniques low-cost a permis au réseau CentipedeRTK de voir le jour. Celui-ci se base sur l‘ouverture et le partage d’une méthodologie de construction d’antennes RTK mises en réseau pour un usage libre et collaboratif, quelle que soit la finalité de son utilisation. Depuis son démarrage en 2019, le réseau ne cesse de croître et ses usages se multiplient dans différents domaines, allant de la recherche forestière jusqu’au suivi des mesures environnementales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".