Bibliographic record
Abstract
Background 1 A map illustrates the study area showing the eight counties in Southern California where the hotspot analysis was performed.6 2 Illustration of the network of roads that intersect through the eight counties of the study area for this study revealing the major roads for each county.8 3 An image showing a wildlife overpass in Canada.12 4 A map of San Diego, Los Angeles, and Ventura Counties displaying road kill points before the Point Density analysis was done revealing a high concentration of road kill in the western portion of San Diego, northern Los Angeles, and southern Ventura Counties.25 5 Using the data (road kill point's shapefile) from the California Road kill Observation System and performing the Point Density analysis.The results demonstrated the greatest density of clustering in five locations, but a high concentration in four areas located in the southwest part of San Diego County.25 6-7 The results for San Diego, Los Angeles, and Ventura Counties are displayed with a high concentration of road kill points in eight major areas on major road networks in each of the counties.26 8-13 Maps were created using the land cover raster overlaid with the roads in each identified cluster area.The results reveal the the land cover types except in each hotspot. .49 14 A map that identified two of the hotspots that were inside the South Coast Missing Linkages project for critical habitats.53 15 Illustration displaying all the layers used for this research as well as the results from the spatial analysis done for this study, which include the Point Density analysis, land cover feature class to raster layer, road kill points, counties shapefile, roads shapefile, and critical wildlife areas.53 viii LIST OF GRAPHS Graphs Pages 1-4 Graphs of San Diego County showing the number of road kill events per land cover type.All identify urban as the land cover type with the highest incidents for road kills in each of the hotspots.30 5-6 Graphs of Los Angeles County showing the number of road kill events per land cover type.Both identify urban as the land cover type with the highest incidents for road kill in each of the hotspots.33 7-8 Graphs of Ventura County showing the number of road kill events per land cover type.Graph 8 identifies agriculture as the land cover type with the highest incidents for road kill, while graph 7 shows urban as the landcover type with the highest incidents of road kill.34
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".