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
The Massasauga (Sistrurus catenatus) is a small rattlesnake in the family Viperidae. Its range includes Ontario, New York, Pennsylvania, Ohio, Indiana, Michigan, Illinois, Wisconsin, Missouri, and Iowa. Although this range is large, the Massasauga has been declining, prompting its recent listing as federally threatened. The primary cause of these declines is habitat loss. In Indiana, the Massasauga is affiliated with emergent wetlands, shrub/scrub wetlands, and prairie, and all of these habitats have dramatically declined. Despite habitat and species declines, recent distributional studies are lacking for Indiana’s populations. As a result, it is unclear where conservation and habitat management efforts should be focused to maximize efforts. I used a combination of visual surveys and Maximum Entropy (MaxEnt) modeling to improve our understanding of the Massasauga in Indiana. Survey efforts relied on historical and current observational records. MaxEnt efforts relied on current observational records. Based on these records, population boundaries were delineated using Geographical Information Systems (GIS). Populations were then prioritized for surveying using date of the most recent observation and extent of suitable habitat. Surveys occurred at high priority and medium-upper priority populations during the spring and summer of 2015 and 2016. A minimum of 40 hours was spent surveying each population unless presence could be confirmed more rapidly. Populations with recent records (between 2010 and 2015) were not surveyed and considered present. These recent observational records were placed within MaxEnt along with several environmental parameters to map the potential distribution of the species. The performance of MaxEnt was assessed and compared using the Area Under the Curve (AUC) and the corrected Akaike’s Information Criterion (AICc). The importance of each environmental variable was also determined and those contributing less than five percent to the model were omitted in later models. Ground validation was also conducted to determine the utility of environmental layers (land cover, national wetland inventory, and satellite imagery) in predicting habitat type and composition within GIS. This allowed the quality of these layers to be assessed. It was also possible to determine if a relationship existed between habitat type or quality and Massasauga presence. Surveys occurred within the boundaries of fifteen populations. Presence could only be confirmed at two of them. Additionally, overall, occupancy was confirmed at only fourteen of 87 historical populations. The area with the greatest occupancy according to distributional records was the northeast part of the state. This trend was also observed in the distribution maps produced by MaxEnt. MaxEnt results indicated that soils, hydrogeology, and presettlement land cover are the most important factors influencing to the distribution of the species. The importance of land cover to the distribution of the species varied among models but was less important than the afore mentioned variables. The distance to a national wetland, created from the national wetland inventory, variable was generally not important. The topographic positioning index and bedrock variables were unimportant, contributing less than five percent to all models. The ground validation results indicate that national wetland inventory, land cover, and orthoimagery layers are of limited utility when used to determine habitat type. Furthermore, habitat composition and quality was not related to Massasauga presence. This latter finding along with the limited influence of land cover to the distribution of the Massasauga, indicate that active season habitat is not the most important determinant of occupancy. The results of this study indicate that the Massasauga is still declining and in need of conservation and expanded management. Furthermore, there a great need to assess additional factors, such as hibernacula selection, that may influence the distribution of the species and the viability of remaining populations.
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 | 0.000 |
| 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 it