Impacts of the herbicide glyphosate on moose browse and moose use of four paired treated-control cutovers near Thunder Bay, Ontario
Bibliographic record
Abstract
Re-assessment of the aerial and ground observations on \nfour paired, glyphosate treated and control, cutovers near \nThunder Bay, Ontario, indicated that aerial tending with \nglyphosate altered the use of these cutovers by moose. \nThe number of pellet groups favoured the control areas (p \n< 0.05) by 1.5 times. Additionally, the number of moose \ntracks and moose track aggregates were more prevalent (p < \n0.05) on the controls for 2 to 3 years after treatment. Pre \nspray data on 2 areas suggested use shifted away from \nglyphosate treated areas. \nBrowse availability was significantly greater (p < 0.05) \non the control plots by 18 times in the highest height class \nmeasured (201 - 350 cm) , 5 times in the next highest (101 - \n200 cm) but not statistically significant (p > 0.05) in the \nlowest (51 - 100 cm), 2 years after treatment. Due to too few \nreplications, differences in availability 1 year after \ntreatment were not statistically significant. \nBiomass of browse removed by moose was 3 to 7 times \ngreater on controls but again these differences were not \nstatistically significant. \nThe average length of moose trails observed in the snow \nwas shorter (p < 0.05) on the controls suggesting less travel \ntime. The size (area) of moose track aggregates was the same \n(p > 0.05) between treatments indicating equal search time \nwhile browsing. \nA carrying capacity model indicated that if all cutovers \nwere sprayed, the treatment would have a negative impact on \nmoose densities. \nGlyphosate treatments should be dispersed to create a \nmosaic of glyphosate treated areas next to non-treated areas. \nSimilarily, areas of seasonal importance such as aquatics, \nsalt licks, and calving areas should have at least a non-sprayed \nbuffer beside them if the adjacent cut area must be \ntreated with glyphosate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".