Eco-environmental landscape characterization data based on artificial intelligence and neural network algorithms
Bibliographic record
Abstract
In this study, the ecological environment landscape pattern index was selected to construct the ecological environment landscape data representation model under the self-organized feature mapping network (SOM) technique.The input data to the model were monitored under unsupervised conditions and made more sensitive to specific characterization information in the neuronal structure (Hebb), which resulted in different groups of regular data.The moving window method shows that the landscape index is unstable under the 1000-3000m window and the magnitude of change begins to decrease at the 4000m scale.The data tends to stabilize at 5000m scale, and the stability of the data decreases at 6000-7000m, and the anomalous data increases at this time.In terms of landscape level, the aggregation and connectivity of the overall landscape of the study area increased and landscape fragmentation, complexity and diversity decreased under the 4000m window.The land use change model based on SOM network can well reflect the law of land use change in the sample area, which greatly expands the spatial analysis research method of land use change.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".