Use of digital multispectral videography to assess seagrass distribution in San Quintín Bay, Baja California, Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it