Determining diatom ecotones and their relationship to terrestrial ecoregion designations in the central Canadian Arctic Islands
Bibliographic record
Abstract
Ecotones are key areas for the detection of global change because many are predicted to move with shifts in climate. Prince of Wales Island, in the Canadian Arctic Archipelago, spans the transition between mid- to high-Arctic ecoregions. We analyzed limnological variables and recent diatom assemblages from its lakes and ponds to determine if assemblages reflected this ecotone. Limnological gradients were short, and water chemistry explained 20.0% of diatom variance in a redundancy analysis (RDA), driven primarily by dissolved organic carbon, Ca and SO4 . Most taxa were small, benthic forms; key taxa such as planktonic Cyclotella species were restricted to the warmer, southern portion of the study area, while benthic Staurosirella were associated with larger, ice-dominated lakes. Nonetheless, there were no significant changes in diatom assemblages across the mid- to high-Arctic ecoregion boundary. We combined our data set with one from nearby Cornwallis Island to expand the study area and lengthen its environmental gradients. Within this expanded data set, 40.6% of the diatom variance was explained by a combination of water chemistry and geographic variables, and significant relationships were revealed between diatom distributions and key limnological variables, including pH, specific conductivity, and chl-a. Using principal coordinates analysis, we estimated community turnover with latitude and applied piecewise linear regression to determine diatom ecotone positions. A pronounced transition was present between Prince of Wales Island and the colder, more northerly Cornwallis Island. These data will be important in detecting any future northward ecotone movement in response to predicted Arctic climate warming in this highly sensitive 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".