FARKLI MLS NOKTA BULUTU YOĞUNLUKLARININ VE KOMŞULUK YÖNTEMLERİNİN KONTROLLÜ SINIFLANDIRMAYA ETKİSİ
Bibliographic record
Abstract
ZETMobil Lazer Tarama (MLS) sistemleri, genellikle kentsel alanlarn ve yol alarnn 3-boyutlu haritalanmasnda tercih edilen, hzl, yksek dorulukta ve yksek younlukta bir veri toplama yntemidir.Bu aratrmada, MLS nokta bulutu snflandrmasnda geleneksel olarak kullanlan yntemlerden olan 2-boyutlu ve 3-boyutlu k-en yakn komuluk (kNN), kresel ve silindirik komuluk yntemleri deerlendirilmitir.lem sresini azaltmak ve beraberinde farkl komuluk hesab yntemlerinin dk younluktaki nokta bulutlarndaki sonuca etkisini deerlendirmek amacyla veri alt rnekleme uygulanmtr.Bu hususta; 3 ana aamada nokta tabanl kontroll snflandrma ilemi gerekletirilmitir: (i) yerel komuluk ilikisinin kurulmas, (ii) znitelik bilgisinin karlmas ve (iii) nokta tabanl snflandrma.Yntemler, ara tabanl MLS sistemleriyle toplanm ve her biri 8 semantik snf ieren TUM-MLS1 ve Toronto-3D nokta bulutlar zerinde test edilmitir.Asl ve farkl younluklardaki alt rneklemlere ayrlan nokta bulutlarndan, belirlenen sabit parametreli yerel komuluk trlerine gre geometrik ve ekil tabanl znitelikler karlm ve Rastgele Orman snflandrma yntemi her bir noktay etiketlemede tercih edilmitir.Sonu olarak, her iki nokta bulutu verisinde de %95,1 genel dorulukta silindirik komuluk yntemi kullanlarak en iyi sonu elde edilmitir.Nokta bulutu younluunun azaltlmasyla ilem sresi bakmndan btn yntemlerde beklendii zere azalmalar gzlenmi olup genel doruluklarda da kayplar olduu gzlemlenmitir
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.201 | 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; both teacher heads agree on what is shown here.
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".