Análise de dados digitais multiespectrais de alta resolução obtidos pelo sensor “Compact Airborne Spectrographic Imager” em área rural do estado do Paraná - Brasil
Bibliographic record
Abstract
The purpose of this study is to analyse, using digital and visual approaches, the data from 7 multispectral bands provided by the Canadian sensor "Compact Airborne Spectrographic Imager". The spectral configuration was from one tested in Canada and the spectral ranges of each of the bands were the following: band 1 (455.0 - 481.4 hm), band 2 (448.4 - 555.5 hm), band 3 (678.4 - 682.0 hm), band 4 (710.7 - 714.3 hm), band 5 (736.0 - 739.6 hm), band 6 (746.9 - 750.5 hm), band 7 (785.0 - 788.6 hm). The study area is located in the Experimental Research Farm from the Federal University of Paraná, located in Pinhais County, 18 km north of Curitiba. A thematic map of the area was elaborated through visual interpretation of stereoscopic aerial photographs 1:8.000, B&W, 23 x 23 cm and 1:2.500 enlarged normal color prints and also detailed fieldwork. The methodology of the research had the following aspects: enhancement techniques; visual interpretation of the individual enhanced bands; defining the possible colour composition using the seven bands and selecting the best color composite; visual comparisons between individual bands and the best color composite. It was concluded that the bands 4 and 5 provided the best results; the best color composite (RGB) was resulted from the bands R3, G6, B7; the genus Araucaria, Eucalyptus e Pinus were mapped due to their low reflectance values and the visual interpretation of the bands comfirmed the correlation values found in the correlation matrix.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".