A picture is worth a thousand data points: an imagery dataset of paired shrub-open microsites within the Carrizo Plain National Monument
Bibliographic record
Abstract
BACKGROUND: Carrizo Plain National Monument (San Joaquin Desert, California, USA) is home to many threatened and endangered species including the blunt-nosed leopard lizard (Gambelia sila). Vegetation is dominated by annual grasses, and shrubs such as Mormon tea (Ephedra californica), which is of relevance to our target species, the federally listed blunt-nosed leopard lizard, and likely also provides key ecosystem services. We used relatively nonintrusive camera traps, or trail cameras, to capture interactions between animals and these shrubs using a paired shrub-open deployment. Cameras were placed within the shrub understory and in open microhabitats at ground level to estimate animal activity and determine species presence. FINDINGS: Twenty cameras were deployed from April 1st, 2015 to July 5th, 2015 at paired shrub-open microsites at three locations. Over 425,000 pictures were taken during this time, of which 0.4 % detected mammals, birds, insects, and reptiles including the blunt-nosed leopard lizard. Trigger rate was very high on the medium sensitivity camera setting in this desert ecosystem, and rates did not differ between microsites. CONCLUSIONS: Camera traps are an effective, less invasive survey method for collecting data on the presence or absence of desert animals in shrub and open microhabitats. A more extensive array of cameras within an arid region would thus be an effective tool to estimate the presence of desert animals and potentially detect habitat use patterns.
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.
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".