Machine learning based approaches for classification of oil spills and microplastics in marine environments
Bibliographic record
Abstract
Environmental modelling is an important approach of environmental engineering and management since it helps gain better understanding of environmental problems and impacts and facilitate environmental decision-making processes. However, because of the intricate conditions enormous data, diverse uncertainties, and various standards and requirements, environmental modeling is usually sophisticated and challenging. This study aimed to develop the novel modelling approaches by integrating machine learning (ML) into analyzing tabular and image datasets for environmental applications. Firstly, a data-driven binary classification approach was developed to analyze oil fingerprinting. After comparing six different machine learning algorithms on five different biomarkers, random forest classifier was found as the most effective and accurate model to distinguish weathered chemically dispersed and non-dispersed oil from the dataset of diamantanes. The developed model was approved to be capable of aiding oil fingerprinting under the studied conditions. It showed the good value of ML methods in environmental modeling especially for oil spill response research and practice. Secondly, an integrated approach by combing the strengths of convolutional neural networks and improved deep convolutional generative adversarial networks was proposed to classify microplastics and oil-dispersant agglomerates (MODAs) with diverse weathering conditions. The f score and model accuracy suggested the robust prediction from the trained model on the dataset of MODAs with different weathering degrees. The results could provide a better understanding of microplastics’ effects on oil fate and transport during a marine oil spill. The proposed approach also presented the high potential of facilitating image-related classification work in environmental fields. This dissertation not only developed two new ML based modelling approaches for environmental applications in oil fingerprinting and oil/microplastics classification, but also demonstrated the high value of ML methods and deep neural networks in processing experimental data for supporting environmental engineering and management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".