Genecology of jack pine in north central Ontario
Bibliographic record
Abstract
To understand the pattern of adaptive variation of jack pine in north \ncentral Ontario better, short-term provenance tests were established. Seed \nwas collected from 64 sites to the east and west of Lake Nipigon and \ngrown in three common garden tests, including a greenhouse trial at \nLakehead University, a farm field trial at Lakehead University, and a field \ntrial near Raith. Eight growth variables were measured (two annual \nheights from the greenhouse trial and three annual heights from each of \nthe field trials), fourteen phenological variables were determined \n(elongation initiation and cessation dates, elongation duration and needle \nflush date at each trial; and foliage purpling at the greenhouse and \nLakehead University field trials), and survival at the Raith trial was \nexamined. Variation expressed among seed sources was significant for all \ngrowth variables and many phenological variables. Multiple regressions \nwere run for 18 of the 23 variables against climatic variables interpolated \nusing geographic information systems techniques from weather data of 56 \nweather stations, as well as spatial, soil, and vegetative variables which \ndescribed the environment at seed origin resulting in coefficients of \ndetermination as high as 0.57. Principal components analysis (PCA) was \nused to summarize the variables examined, with 33 and 21 per cent of the \nvariation accounted for by the first and second component respectively. \nMultiple regressions were run on the factor scores produced from PCA \nagainst the variables describing environment at seed origin. These \nregression models had coefficients of determination of 0.323 and 0.429 \nfor the first and second factor scores respectively. The pattern of \nvariation in this portion of the range as displayed in the mapping of the \npredicted factor scores was clinal with numerous irregularities. July and \naverage annual temperatures, heating degree days, frost dates, and soil and \nvegetation variables were included in the predictive models. The contrast \ndisplayed in height performance between seedlings from the southwestern \nportion of the range and those from the north shore of Lake Superior \nreflects trends seen in a previous study of cone and needle characteristics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".