Chironomid‐based inference models for estimating end‐of‐summer hypolimnetic oxygen from south‐central Ontario shield lakes
Bibliographic record
Abstract
1. Subfossil chironomid head capsules were sampled from surficial sediments from 86 boreal shield lakes in south‐central Ontario, Canada. Lake characteristics ranged from shallow to very deep (> 80 m), ultraoligotrophic to mesotrophic, and with end‐of‐summer hypolimnetic oxygen conditions ranging from near‐saturation to anoxic. 2. Subfossil chironomid assemblages, comprising 44 taxa from 59 lakes, were analysed using multivariate ordination techniques such as redundancy analysis (RDA) and canonical correspondence analysis (CCA). Forward selection in RDA and CCA both showed that measures of oxygen, such as end‐of‐summer volume‐weighted hypolimnetic oxygen concentration (VWHO) and bottom oxygen concentration (botO2), were the strongest explanatory variables for the chironomid data. Maximum depth and major ion chemistry were also important explanatory variables. 3. Oxygen inference models were developed using partial‐least‐squares regression (PLS), weighted‐averaging partial‐least‐squares regression (WA‐PLS), and weighted averaging regression (WA). Models were developed using both the full 44 taxa assemblage (which included littoral taxa) and using only 15 profundal‐type taxa. 4. Cross‐validated models (jackknifing) using full‐assemblage or profundal‐only taxa had similar statistical power (similar root mean squared error of prediction, RMSEP). The best models had moderate predictive power, with an r2jack as high as 0.56, and an RMSEP as low as 2.15 mg L–1 for [VWHO], and an r2jack of 0.49 and an RMSEP of 0.24 for log([botO2] + 1). 5. Reconstruction of [VWHO] and [botO2] using a previously published chironomid profile that showed strong lake response to land‐clearance and logging suggests that oxygen inference models are reliable and accurate, reflecting the qualitative changes occurring in subfossil assemblages. However, the profundal‐only models may be misleading in situations where the ratio of littoral‐to‐profundal subfossils changes drastically in response to lake disturbance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".