Marsh Ecology Research Program (MERP): Phytoplankton data (1985-1989)
Bibliographic record
Abstract
The Marsh Ecology Research Program (MERP) was a long-term interdisciplinary study on the ecology of prairie wetlands. A scientific team from a variety of disciplines (hydrology, plant ecology, invertebrate ecology, vertebrate ecology, nutrient dynamics, marsh management) was assembled to design and oversee a long-term experiment on the effects of water-level manipulation on northern prairie wetlands. Ten years of fieldwork (1980 -1989), combining a routine long-term monitoring program and a series of short-term studies, generated a wealth of new and diverse information on the ecology and function of prairie wetlands (Murkin, Batt, Caldwell, Kadlec and van der Valk, 2000). This data set includes phytoplankton data, collected as part of the algae secion of MERP. The objectives of the Marsh Ecology Research Program algal study were to unveil a better understanding on how different algae assemblages contribute to primary production (measured as C-assimilation rates) and biomass (chlorophyll-a content) in a prairie marsh habitat. The undertaking of this study also aimed to show how algal biomass would change with varying water depth, macrophyte density, and light attenuation. This unique project allowed for the productivity of these algal communities to be compared with that of the macrophyte community in order to demonstrate their importance in sustaining prairie wetland food webs (Robinson et al., 1997a). For further information on the Marsh Ecology Research Program (MERP), please visit: http://www.ducks.ca/conserve/research/projects/merp/index.html References: Murkin, H.R., B.D.J. Batt, P.J. Caldwell, J.A. Kadlec and A.G. van der Valk. 2000a. Introduction to the Marsh Ecology Research Program. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds) H.R. Murkin, A.G. van der Valk and W.R. Clark. pp. 3-15. Ames: Iowa State University Press. Robinson, G.G.C., Gurney, S.E., and Goldsborough, L.G. 1997a. The primary productivity of benthic and planktonic algae in a prairie wetland under controlled water-level regimes. Wetlands 17 (2): 182-194.87. Resulting Publications on Algae Assemblages Gurney, S.E., and Robinson, G.G.C. 1988. The influence of water level manipulation on metaphyton production in a temperate freshwater marsh. Verh. Internat. Verein. Limnol. 23:1032-1040. Hosseini, S.M., and A.G. van der Valk. 1989a. The impact of prolonged above-normal flooding on metaphyton in a freshwater marsh. In Freshwater Wetlands and Wildlife. (Eds.) R.R. Sharitz and J.W. Gibbons. pp. 317-324, USDOE Symposium Series 61, Oak Ridge: USDOE Office of Scientific and Technical Information Hosseini, S.M., and A.G. van der Valk. 1989b. Primary productivity and biomass of periphyton and phytoplankton in flooded freshwater marshes. In Freshwater Wetlands and Wildlife. (Eds.) R.R. Sharitz and J.W. Gibbons. pp. 303-315. USDOE Symposium Series 61, Oak Ridge: USDOE Office of Scientific and Technical Information. Robinson, G.G.C., Gurney, S.E., and Goldsborough, L.G. 1997a. The primary productivity of benthic and planktonic algae in a prairie wetland under controlled water-level regimes. Wetlands 17 (2): 182-194.87. Robinson, G.G.C., Gurney, S.E., and Goldsborough, L.G. 1997b. Response of benthic and planktonic algal biomass to experimental water-level manipulation in a prairie lakeshore wetland. Wetlands 17 (2): 167-181.86 Robinson, G.G.C., Gurney, S.E., and L.G. Goldsborough,. 2000. Algae in Prairie Wetlands. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds.) H.R. Murkin, A.G. van der Valk, and W.R. Clark, pp. 3-15. Iowa: Iowa State University Press.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".