Landscape scale lake surveys of Algonquin Provincial Park
Bibliographic record
Abstract
To better understand the status of Lake Trout and Brook Trout populations across the Algonquin Provincial Park landscape 192 index netting surveys were conducted on 161 lakes utilizing standardized multimesh benthic gillnets between the years 2009 and 2022. Index netting was conducted using a depth-stratified randomized site survey design and employed multi-pass sampling on a majority of the lakes to provide the opportunity for occupancy analysis. Two primary netting methods were employed; Summer Profundal Index Netting (SPIN) for Lake Trout populations sampled between 2009-2012; and a modified Ontario Broadscale Monitoring (BsM) method we refer to as Short Duration Point Sampling (SDPS). The SDPS method uses the large mesh BsM nets (NA1) deployed for a one-hour duration within the same depth strata used in the BsM program with a sampling intensity in each stratum proportional to the surface area. The overall sampling intensity (nets/lake) is greater than that employed in the BsM program as we are interested in lake-specific analyses. This data dryad provides general information on each lake sampled including lake characteristics, lake volumes, netting site locations, and spring water chemistry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.002 |
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".