Correlation of environmental variables with Heteromastus filiformis habitat density: Gap-filling and machine learning approaches
Bibliographic record
Abstract
ABSTRACT Benthic invertebrates have been used as ecological indicators to assess marine ecosystem health. However, the intricate interactions between these organisms and their environment complicate detailed analyses of the effects of environmental disturbance events. This study investigates the impact of various environmental variables on the habitat density distribution of Heteromastus filiformis using gap-filling and machine learning approaches. Data collected from the Korea Strait, Yellow Sea, and East Sea were analyzed to forecast habitat density using various machine learning models combined with different gap-filling methods and feature selection. The results show that the artificial neural network model with a multilayer perceptron architecture outperforms others, achieving a weighted mean absolute error of 0.865 and a weighted accuracy of 0.631 in predicting Heteromastus filiformis habitat density when combined with station-wise correlated feature gap-filling method and the selection of 16 environmental features. The model also forecasts that simultaneous increases in ocean salinity and water depth will have the most detrimental impact on its habitat density compared to other environmental features. This study demonstrates the feasibility and effectiveness of machine learning techniques in bridging data gaps and enhancing the understanding of influential factors in benthic marine ecosystems.
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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 | 0.000 | 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 | 0.002 |
| 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".