Upland boreal forest northwest of Thunder Bay, Ontario : ecology and applications to silviculture / by Jeffery C. G. Goelz. --
Bibliographic record
Abstract
Multivariate phytosociological methods were used \nto investigate the ecology of upland boreal forest \nstands. The ecological information was used to derive \nsilvicultural recommendations. The boreal forest stands \ndid not form tight associations. Species were distributed \nindividualistically; most species have broad, overlapping, \nenvironmental tolerances. Most of the variability among \nstands was attributed to the environment and to species \nprecedence on a site. Geomorphology and moisture regime \nwere related to community composition. Pinus banksiana \ndominates sandy glaciofluvial deposits. Picea mariana \nachieves moderate abundance on glaciofluvial deposits \nwhich are moister due to finer soils or to topographic \nposition. picea mariana may also dominate shallow \nmoraines. Deeper moraines were dominated by mixedwoods \ncomposed of all species common to uplands in the study \narea. Succession is of minimal importance; other factors \noverride successional trends. \nWhile plant communities were related to \nthe landforms are much more discrete than \ncommunities. Therefore, landforms were used \nsilvicultural recommendations. Land types were \nby combining or dividing simple features. The seven land types were \nassociated with trends of community composition and of productivity. \nSilvicultural recommendations were derived for each of \nthese land types. These recommendations were primarily \ndetermined by potential hardwood competition and \nproductivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".