GIS Technology in Maritime: A MET Innovation at MAAP
Bibliographic record
Abstract
This paper introduces Maritime Academy of Asia and the Pacific (MAAP), its curriculum and research and extension initiatives including but not limited to one of MAAP latest best practices in introducing geographic information system (GIS) to its community (internal and external) who share similar passion for MET innovation. The various GIS–based accomplishments led and shared by MAAP as part of its Research and Extension Services (RES) initiatives are presented namely: several GIS-based papers presented and published both local and international; GIS-based project proposals for campus management enhancement prepared for MAAP, Bataan Peninsula State University (BPSU), Lyceum University of the Philippines (LPU) and Catanduanes State University (CSU); capability Training on GIS for MAAP community and other interested institutions; and Commission on Higher Education (CHED) and Department of Science and Technology (DOST)- endorsed National 3-day GIS Conference are also discussed. There are 78 proposed GIS-based research project workshop outputs currently being implemented by 20 higher educational institutions (HEIs) in the Philippines, 17 of which are applicable to any maritime education and training institutions (METIs). MAAP has six on–going GIS- based special academic research and development projects which this paper intends to share to co-AMFUF members.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".