A trail camera imagery dataset of contrasting shrub and open microsites within the Carrizo Plain National Monument, San Luis Obispo County, California
Bibliographic record
Abstract
Abstract Background Carrizo Plain National Monument is one of the largest remaining patches of San Joaquin Desert left within the Central Valley of California. It is home to many threatened and endangered species including the San Joaquin kit fox, blunt-nosed leopard lizard, and giant kangaroo rat. The dominant plant lifeform is shrubs. The species Ephedra californica comprises a major proportion of the community within this region and likely also provides key ecosystem services. We used motion sensor trail cameras to examine interactions between animals and these shrubs. This technology is a less invasive alternative to other animal surveying methods such as line transects, radio tracking, and spotlight surveys. Cameras were placed within the shrub understory and in the open (i.e. non-canopied) microhabitats at ground level to estimate animal activity. Findings Trail cameras were successful in detecting the presence of animal species at shrub and open microhabitats. A total of 20 cameras were deployed from April 1 st , 2015 to July 5 th , 2015 at paired shrub/open microsites at three locations along Elkhorn Road in Carrizo Plain National Monument (35.1914° N, 119.7929° W). Each independent site was approximately 1 km 2 . Over 440,000 pictures (both of animals and triggers from vegetation moving in the wind) were taken during this time. The trigger rate was very high on the medium sensitivity camera setting in this desert ecosystem, and the rates did not differ between shrub and open microsites. The raw data (.jpeg images) are publicly available for download from GigaDB. Conclusions Motion sensor trail cameras are an effective, non-invasive alternative survey method for collecting data on presence/absence of desert animals. We detected mammals, reptiles, birds, and also insects in 0.4% of the images. We also successfully detected the Federally-listed blunt-nosed leopard lizard. A more extensive array of cameras within the Carrizo Plain National Monument could thus be an effective tool to estimate the presence of this species along with the presence of other animals.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".