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.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".