Temporal analysis on spectral reflectance of clove vegetation based on landsat 8 imagery
Bibliographic record
Abstract
This study aims to analyze temporally the spectral reflectance of clove vegetation using Landsat 8 multitemporal imagery data in Buleleng district, Bali. The analysis method uses the conversion of raw data from Landsat 8 images to the spectral reflectance value at the Top of Atmosphere (TOA). This conversion scales back the pixel values ??of the Landsat 8 image in the visible spectrum, namely bands 2, 3, 4 and infrared bands 5, 6, and 7 into percentage units. The temporal analysis technique is carried out by grouping the time series of Landsat 8 image data for 1 period, in 2015, into 4 quarterly groups based on the acquisition time, namely Quarter I (January, February, March), Quarter II (April, May, June), Quarter III (July, August, September) and Quarter IV (October, November, December). The results showed that the graph pattern of the average percentage of spectral reflectance in each quarter was the same and in the infrared spectrum was greater than the visible spectrum. The average value of the largest spectral reflectance was found in the second Quarter which was acquired by band 5 of 28.143%, while the smallest in the first Quarter which was acquired by band 2 was 2.503%.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".