Multidimensional analysis of remote sensing monitoring data and quantitative assessment of ecological effects in marine ecological reserves
Bibliographic record
Abstract
Based on the wide application of satellite remote sensing in the field of ecology and environment, the study builds a remote sensing monitoring system for marine ecological reserve, processes the marine remote sensing data by using the technologies of GeoTools, NetCDF and GeoServer, builds a WebGIS system, and collects, transmits and integrates and processes the marine ecological data through the data collection module and the visualization query module.Selecting Laizhou Bay as the study area, the system of this paper was used to collect and process the ecological remote sensing data within the study area during the period of 2003-2022, and to carry out multidimensional analyses including the factors of sea surface temperature and photosynthetically active radiation, sea surface salinity and degree of eutrophication, and so on.A marine ecological health assessment index system was constructed to assess the ecological health of Laizhou Bay and explore its spatial and temporal distribution characteristics.During the period 2003-2022, sea surface temperature and photosynthetically active radiation (AWEI) in the Laizhou Bay region showed an overall increasing trend, and sea surface salinity showed a slight decreasing trend.The total area of mariculture and the total area of zizyphus culture were generally on the rise, and the eutrophication of the water body in Laizhou Bay was most serious in 2013, with the AWEI reaching the maximum value (1.00), which was mitigated after 2013, and the AWEI was reduced to 0 in 2022.The integrated health index (IHI) of the ecosystem of Laizhou Bay increased gradually in 2003, 2012 and 2022, and the health status changed to "healthy", and the area of healthy zone expanded to "healthy".In 2003, 2012 and 2022, the integrated health index (IHI) of the Laizhou Bay ecosystem gradually increased, the health status changed to "healthy", and the area of the healthy zone expanded by 54.54%.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".