Corrigendum: Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values
Bibliographic record
Abstract
International roughness index (IRI) values were calculated from multi-step processing of accelerometer data collected using three smartphone devices in three consumer vehicles under 11 test scenarios on a 1000 m stretch of secondary highway in New Brunswick. These data were compared to IRI data from a Class 1 inertial profiler averaged over 1000 m (2.60 m/km, std. dev. = 0.029). The combinations of factors producing average IRI values closest to Class 1 inertial profiler were the compact car, Galaxy SIII, windshield mount, at 80 km/h (2.58 m/km, std. dev. = 0.075) and the SUV, iPhone 5, windshield mount, at 50 km/h (2.63 m/km, std. dev. = 0.054). Changes in device type, vehicle type, and mounting arrangement significantly impacted IRI variance, while vehicle speed (50 km/h and 80 km/h) did not. The development of correction factors and analysis automation could make these devices a low-cost option for real-time network-level pavement management. Resume : Les valeurs de l'indice de rugosite international (IRI) ont ete calculees en plusieurs etapes apartir de donnees d'accelerometre recueillies en utilisant trois telephones intelligents dans trois vehicules commerciaux reguliers selon 11 sce- narios sur un segment de 1000 m d'autoroute secondaire au Nouveau-Brunswick. Ces donnees ont ete comparees aux donnees IRI d'un profilometre inertiel de Classe 1 dont la moyenne a ete etablie sur 1000 m (2,60 m/km, ecart-type = 0,029). Les combinaisons de facteurs produisant les valeurs IRI moyennes les plus pres de celles du profilometre inertiel de Classe 1 ont ete la voiture compacte avec Galaxy SIII installe sur le parebrise a ` 80 km/h (2,58 m/km, ecart-type = 0,075) et le VUS avec iPhone 5 installe sur le parebrise a ` 50 km/h (2,63 m/km, ecart-type = 0,054). Les changements de type de dispositif, de type de vehicule et de montage ont eu un impact important sur la variance de l'IRI, alors que a vitesse du vehicule (50 et 80 km/h) n'ont eu aucun effet. Le developpement des facteurs de correction et l'automatisation de l'analyse permettrait d'utiliser ces dispositifs afiables couts pour la gestion, en temps reel, des chaussees d'un reseau routier. (Traduit par le Redaction) Mots-cles : indice de rugosite international, telephone intelligent, profilometre inertiel, faible cout, gestion des chaussees.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".