Bibliographic record
Abstract
Human recreational activities and tourism are concentrated on the islands and in coastal waters, oftendepending on the maintenance of high water quality. The managing of impacts of urbanization andindustrialization on the coastal zone ecology has become a high priority for many nations such as Malaysia and,hence, the need to develop better methods for monitoring and predicting change in islands and their coastalsystems. Many of the dynamics of the open ocean, islands and changes in their coastal areas can be mappedand monitored using remote sensing techniques. Hyperspectral imaging is a tool that can provide an increasingnumber of marine and coastal properties over a spatial and temporal range. The remote-sensing measurementsof some selected Sabah islands and their coastal waters were collected using a 4 kg “bread-box” sizedUPM-APSB’s AISA (Airborne Imaging Spectroradiometer for different Applications) airborne spectrographicimager where it was flown over the islands of Bohey, Mabul, Pom-Pom, Kulapuan, Omadal and Larapan studyareas as part of the 2004 Sabah’s “Ops Pasir” inaugural flight experiment in Sabah on July 13, 2004. Thepurpose of the study was to determine the current capabilities of a locally developed UPM-APSB’s AISAairborne hyperspectral remote sensing applications to operationally map and monitor the islands in Sabah andobserve the status of their coastal waters and reef environment. The airborne data were pre-processed on-boarda fixed wing aircraft and later processed using spectral end member during the advance digital processingtechniques. AISA AeroMAPTM research products showed that the current technology did a good job ofconveying spatial variability of the parameters being tested such as human activities and impact, presence offishing boats, coral reef, near shore shallow bathymetry, shoreline features and coastal vegetation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".