Bibliographic record
Abstract
从卫星传感器、大气校正、光学特性测量、生物光学模型及水体辐射传输、水质参数反演方法等方面,系统分析了湖泊水色遥感的发展现状.湖泊水体物质组份的复杂性以及卫星传感器与实际需求的矛盾决定了湖泊水色遥感的难度.目前湖泊水色遥感在一些关键问题上仍没有实质性进步,离水色遥感监测的业务化尚有一段距离.令人欣慰的是,卫星传感器以及水色遥感反演算法的不断发展和进步,让我们看到了胜利的曙光.;We analyzed in detail the status quo of lake water color remote sensing from some aspects of satellite sensor, atmospheric correction, optical properties measurement, bio-optical model, radiative transfer model for the waters, and water quality parameter retrieval approach. It has a great difficulty to have practical application of water color remote sensing at a regional scale at present, depending on the complex components of lake water and on the inconsistency between satellite sensor and its actual demand from water quality monitoring. The progress in some key problems for lake water color remote sensing is still small, and there is a long way to go in applications of lake water color remote sensing. However, to be greatly pleasure, the satellite sensor and water color parameter retrieval approach are developing and making progresses, and the application in the future is hopeful.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".