An investigation of the snowpack signal in moisture-sensitive trees from the Southern Canadian Cordillera
Bibliographic record
Abstract
Variations in mountain snowpack in the western Canadian Cordillera have widespread and important impacts on ecosystems, environmental processes and socio-economic activities (e.g. water availability downstream). Historical records of snowpack generally span only the latter half of the 20th century offering a limited perspective on the causes and uniqueness of recently observed changes across the region. This paper explores the potential utility of a network of low elevation Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa) tree ring-width chronologies to reconstruct past snowpack variations. Correlation coefficients between the tree-ring chronologies and a set of snow water equivalent (SWE) records are calculated and mapped. Separate analyses were carried out for total ring- width (TRW) and partial-ring measurements (earlywood and latewood; EW and LW). A set of Adjusted LW chronologies was also developed; in these, the relationship between LW and the preceding EW width has been removed. The ring-width chronologies exhibit moderately strong relationships with SWE records from the western Canadian Cordillera and these relationships vary in sign across the region. Distinctive regional groups are identified where chronologies exhibit same-sign correlations with SWE, in possible accordance with the elevation and characteristics of the tree-ring chronology sample sites. The EW chronologies correlate more strongly and consistently with SWE records in regions where the growth relationship with SWE is negative. The LW chronologies, and particularly the Adjusted LW chronologies, exhibit a greater number of positive correlations with the set of SWE records. Collectively these results offer valuable insights for developing a targeted sampling and/or reconstruction strategy that can exploit these different relationships with SWE to generate more robust estimates of pre-instrumental snowpack for the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".