Long-term monitoring of lemming abundance and reproductive activity on Bylot Island, Nunavut
Bibliographic record
Abstract
We monitor lemming abundance and demography using three methods. (1) Dead trapping using snap-traps at 3 sites on the island in mid to late July (since 1993); we cumulate at least 500 trap-nights at each site annually by trapping over periods ranging from 3 to 10 days. (2) Capture-mark-recapture by live trapping animals using Longworth traps on three 7 to 11 ha grids (100 to 144 traps/grid) in wetland (1 grid) and mesic (2 grids) habitats since 2004. Trapping sessions last for 3 consecutive days (4 or 5 days in 2004 to 2007) and are repeated three times (4 times in 2005 to 2007) during the summer from mid-June to mid-August. In 2015, we added three 7 ha grids (96 to 100 traps/grid) in mixed habitats in 3 additional sites where trapping is conducted during one trapping session. The species, age, sex and reproductive condition of all captured animals is also determined. One of the live-trapping grids in mesic habitat was subjected to various experimental manipulations. From 2007 to 2011, snow fences were used to enhance snow depths in order to determine its effect on lemming abundance and distribution. Snow fences (1.3 m high) was erected in 6 rows of fencing 270 m long each and perpendicular to the prevailing wind (spacing varies between 30 and 50 m, with greater spacing toward prevailing wind). Since 2012, the grid used for the snow fencing experiment is now used for a predator control experiment. All terrestrial and avian predators (except ermine) are excluded by a 1.3 to 2-m high fence made of chicken wire surrounding the grid and covered by criss-crossing fishing line spaced every 50 cm on top. (3) Survey of lemming winter nests after snow-melt in early July using the line transect method since 2007. Each transect is permanent (since 2009), is 500 m long and randomly located within each habitat. A total of 60 line transects is surveyed in wetlands, mesic tundra and streams in mesic tundra (20 transects/habitat). In 2007 and 2008, number of transects were 75 (25 per habitat) and 30 (10 per habitat), respectively. The exact position of each nest found along the transect is noted. In addition, each spring, all winter nests are systematically counted on our live trapping grids by walking parallel lines 5 m apart. Winter nests found opportunistically are also collected in years of low lemming abundance. All winter nests are dissected to determine the lemming species using the nest, reproductive activity (based on pellet size) and signs of predation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".