MétaCan
Menu
Back to cohort
Record W3198471756

The Impact of Nutrient Loading from Canada Geese (Branta canadensis) on Water Quality, a Mesocosm Approach

2006· dissertation· en· W3198471756 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSUNY Digital Repository Support (State University of New York System) · 2006
Typedissertation
Languageen
FieldEnvironmental Science
TopicIntegrated Water Resources Management
Canadian institutionsnot available
Fundersnot available
KeywordsBrantaMesocosmNutrientEnvironmental scienceWater qualityEcologyBiologyGoose
DOInot available

Abstract

fetched live from OpenAlex

We conducted a mesocosm experiment to determine the impact of Canada Goose (Branta cana.densis) feces on water quality parameters. After 30 days of fecal additions (treatments of 2.419 g, 1.209 g and 12.090 g every 3 d) we found no significant impact on soluble reactive phosphorus, total phosphorus, ammonia, nitrate, total Kjeldahl nitrogen, chlorophyll-a, phycocyanin or turbidity for any of the treatment groups versus the control (no fecal addition). Nitrogen to phosphorus ratios were not affected by the fecal additions. Although there was no significant increase in chlorophyll-a concentration or phytoplankton biovolume, there was an increase in phytoplankton counts in the high treatment group. Phytoplankton diversity (using the Shannon index of diversity) was significantly decreased by the addition of goose feces (H1'=0.575, H2'=0.433, t=l7.43, p< 0.001, where H1' is the control and H2' is the 12.090 g treatment). We performed a settling experiment which suggested that nutrients in goose feces settle to the sediment quickly, prohibiting uptake by phytoplankton which explains the apparent lack of impact of fecal additions on water quality. Since most of the nutrients in goose feces settle to the sediment, it is likely that the impact of the nutrients will not become evident until a mixing event occurs or a benthic food web passes them to the organisms of the water column.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.194
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it