Use of digital multispectral videography to assess seagrass distribution in San Quintín Bay, Baja California, Mexico
Bibliographic record
Abstract
Apparent threats to the spatial distribution of seagrass in San Quintín Bay prompted us to make a detailed assessment of habitats in the bay. Six coastal habitats and three seagrass subclasses were delineated using airborne digital multispectral videography (DMSV). Eelgrass, Zostera marina, was the predominant seagrass and covered 40% (1949 ha) of the areal extent of the bay in 1999. Eelgrass grew over a wide range of tidal depths from about –3.0 m mean lower low water (MLLW) to about 1.0 m MLLW, but greatest spatial extent occurred in intertidal areas –0.6 m to 1.0 m MLLW. Exposed-continuous (i.e., high density) eelgrass was the most abundant habitat in the bay. Widgeongrass, Ruppia maritima, was the only other seagrass present and covered 3% (136 ha) of the areal extent of the entire bay. Widgeongrass grew in single species stands in the upper intertidal (≥ 0.4 MLLW) and intermixed with eelgrass at lower tidal depths. Overall accuracy of the six habitat classes and three subclasses in the DMSV map was relatively high at 84%. Our detailed map of San Quintín Bay can be used in future change detection analyses to monitor the health of seagrasses in the bay.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".