Off-channel aquatic habitat use by river otters and other vertebrates in the Central Platte River Valley, NE
Bibliographic record
Abstract
We employed a multi-camera non-linear array sampling design to understand river otter habitat-use patterns in natural and artificial off-channel wetlands along the Platte River. We surveyed 10 off-channel aquatic habitats between 13 February to 15 October 2019. The sites were comprised of 5 pond and 5 slough sites. Each site consisted of a 100-130-meter non-linear array of 3 camera traps placed on existing wildlife trails along the banks of an off-channel aquatic habitat. Individual cameras were spaced between 40 and 75 meters apart within each array, depending upon conditions (availability of animal trails and appropriate topography). Habitat variables were collected at two spatial scales including the macrosite (broader aquatic environment; n = 10) and the microsite (trail along terrestrial-aquatic boundary; n = 30). In addition to documenting river otter occurrences, we recorded all identifiable vertebrate species from camera trap surveys. Therefore, this database has habitat modeling value for a range of other species including mammals such as the American Mink (Neovison vison), American Beaver (Castor canadensis), and Muskrat (Ondatra zibethicus) as well as birds such as the Great Blue Heron (Ardea herodias) or Canada goose (Branta canadensis). Some habitat variables are congruent across spatial scales and others differ. Macrosite habitat variables included maximum and average water depth (m), total water surface area (ac), water pH, water total hardness, mean distance to the river (m), mean distance to dirt road (m), mean distance to paved road (m), mean distance highway (m), mean distance to building (m), mean distance to woodland (m), mean vegetation cover across multiple height classes, the proportional cover of graminoids, forbs, woody species, and groundcover as litter. Microsite variables included all the same distance and vegetation cover metrics but also included bank slope, bank height (m), and control variables like the trail camera height and distance of the camera to the trail. The wetland depth profile was not considered at the microsite level as it reflected features of the larger waterbody. We included river otter scat detection as a validation technique for our river otter relative use metrics which included river otter presence/absence, total river otter captures per camera month, river otter young presence/absence, and river otter young captures per camera month.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.017 |
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 teacher head, 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".