Climate reconstructions based on postglacial macrofossil assemblages from four river systems in southwestern Alberta
Bibliographic record
Abstract
Following floods experienced in southwestern Alberta in June 2013, 36 palaeoenvironmental sediment samples were collected from the banks of four affected streams: the Kananaskis River, Highwood River, Tongue Creek and Jumpingpound Creek. The sampled layers were deposited during the Holocene, and provide evidence of riparian ecology in the region since the end of the last glacial interval. The samples were processed to extract sub-fossil macroremains (including seeds, fruits, and aquatic and terrestrial mollusc shells). Macrofossils are particularly useful for creating fine-resolution (site-specific) reconstructions. An innovative aspect of this study was the derivation of regional bioclimatic trends through time. This was achieved by applying a dual-layered weighted calibration function which incorporated the relative productivity of indicator taxa, their modelled climatic optima, and the influence of climate (temperature and precipitation) on their ecological niche. Optima and climatic influence values were calculated using the machine-learning maximum entropy environmental niche modelling tool, MAXENT, trained on global occurrence records for the taxa. This study aimed to address gaps in prior palaeoenvironmental research, allowing for a spatially explicit quantitative reconstruction of climate in the transitional foothill region near Calgary. Results indicated warm conditions immediately following glacial retreat, followed by an early to mid-Holocene cooling which coincided with the driest interval. Temperature and moisture regimes oscillated in the mid-Holocene, exhibiting the highest values for both parameters. Increased moisture was at least in part prompted by increased input from summer glacial meltwater (the result of higher summer temperatures) which added to the riparian water budget. The mid- to late Holocene, and up to modern times, showed a gradual decrease in both moisture and temperature, until stabilizing near the Holocene average.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".