Assessment of pixel-oriented k-NN machine learning algorithm performance for the interannual remote sensing monitoring of eelgrass beds at the mouth of the Romaine
Bibliographic record
Abstract
Eelgrass cover extent is among the most reliable indicators for measuring changes in coastal ecosystems. Eelgrass has colonized the mouth of the Romaine River and has become a part of environmental monitoring there since 2013. The presence of eelgrass in this area is an essential factor for the early detection of changes in the Romaine coastal ecosystem. This will act as a trigger for an appropriate environmental response to preserve ecosystem health. In this paper, a cost- and time-efficient workflow for such spatial monitoring is proposed using a pixel-oriented k-NN algorithm. It can then be applied to multiple modellers to efficiently map the eelgrass cover. Training data were collected to define key variables for segmentation and k-NN classification, providing greater edge detection for the presence of eelgrass. The study highlights that remote sensing and training data must be acquired under similar conditions, replicating methodologies for collecting data on the ground. Similar approaches must be used for the zonal statistic requirements of the monitoring area. This will allow a more accurate and reliable assessment of eelgrass beds over time. An overall accuracy of over 90% was achieved for eelgrass detection for each year of monitoring.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".