Role of aquatic macrophytes in trophic status of Northwestern Ontario lakes
Bibliographic record
Abstract
This research focusses on the development, refinement, and assessment of regional trophic status \nmodels for lakes of northwestern Ontario. Two companion papers describe the application of \nimage analysis of aerial photographs as a technique for mapping aquatic plant distribution, and the \ndesign of an innovative sampling device to simplify the collection of macrophytes. \nTrophic status models were developed for lakes of northwestern Ontario, based on empirical \nrelationships between Secchi disk transparency, total phosphorus, and chlorophyll a concentration. \nCorrective terms were added to the equations to adjust for the effect of water colour on Secchi \ndisk transparency, and the effect of aquatic macrophyte abundance on the chlorophyll - \nphosphorus relationship. The adjusted models demonstrated an improvement in performance, as \nmeasured using standing stock of benthos as a response variable. These models would be \nexpected to have applicability across the Precambrian Shield region of Canada. \nDistributional maps of aquatic vegetation were produced for area lakes using digital image analysis \nof aerial photographs. Recent improvements in the price and performance of computer hardware \nand software make this a viable alternative to the conventional, visual mapping technique. The \nmaps produced are highly detailed, with differentiation to species possible in some instances. \nCertain species may be further partitioned into density classes. The authenticity of the maps \ndepends on the ability to properly define the spectral 'signatures' for different macrophyte types. \nThese signatures were most easily defined for floating-leafed and emergent forms; submersed \nvegetation proved more difficult to classify. The main detriment to this approach is the steep \nlearning curve associated with the image analysis software. A thorough description and assessment of this technique is provided, with a discussion of its merits and deficiencies when \ncompared with the conventional visual interpretive method. \nA portable macrophyte sampler was designed for use in the relatively inaccessible lakes of this \nregion. As such, it was required to be lightweight, easily transportable, and useable by a single \nperson. The device is effective for obtaining quantifiable biomass samples of most rooted aquatic \nplants over a wide variety of substrates and sampling depths. Details on the design, operation, \nand performance of the sampler are documented within.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".