Researching Chlorophyll-Α Levels In Freshwater Lakes To Understand The Environmental State Of The Turtle Mountains Using NDCI Through Remote Sensing Techniques
Bibliographic record
Abstract
Chlorophyll α is one of the main indicators of water quality. Increasing levels have various triggers such as agricultural runoff, wastewater from treatment plants, sewage, and humus. Addressing chlorophyll α is crucial since high concentrations are precursors to eutrophication and are hazardous to human health. The Turtle Mountains in North Dakota and Manitoba is an island forest laden with lakes and trees. However, the noticeable extent of agricultural surface on the United States side of the border has made obvious the disintegration of forested area. Also, a lack of information and studies do not allow anyone to comprehend the level of damage the Turtle Mountains have already tolerated. Saving them could be too late.For this study, evaluating water conditions is the main avenue for initiating an understanding of the Turtle Mountains and its environmental status, primarily by gaining a sense of chlorophyll α levels in the area. Completing this requires computing the Normalized Difference Chlorophyll Index through remote sensing. NDCI is meant to give a sense of chlorophyll α and is especially useful for inland waters. Mean NDCI values are compiled and graphed for lakes 0.027 square kilometers or 27,000 square meters or larger. The images used cover from around 1985 to 2021 and are during the months of June, July, and August. Using the mean NDCI values from lakes of the Turtle Mountains, two statistical tests are applied for diagnostic purposes. The Mann-Kendall statistical analysis is conducted to determine whether or not the values have significantly changed overtime. The difference of means test, or t-test, is completed to determine if there is a significant difference between average values on either side of the Canadian-United States border and between lakes surrounded with trees and lakes surrounded with agriculture. When considering chlorophyll α, agriculture is normally believed to be a primary culprit for poor water quality, and more lakes on the U.S. side of the border are surrounded by agriculture than lakes on the Canadian side of the border. As suspected, lakes in North Dakota generally have higher NDCI values then lakes in Manitoba. However, upon further analysis, trees appear to be increasing chlorophyll α concentration more than agriculture. Furthermore, NDCI values are decreasing overtime. The reason for this could be the amount of humus gathering in the freshwater lakes, which is notorious for agitating chlorophyll content. Humus is readily supplied by vegetation such as trees rather than crops form cropland. Plus, lowering NDCI overtime could be a response from ongoing deforestation. In any case, the main issue is knowing what approach to use to honestly determine the environmental state of the Turtle Mountains, especially since monitoring chlorophyll α alone does not provide an answer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".