Pasture for horses: an underestimated land use class in an urbanized and multifunctional area
Bibliographic record
Abstract
This paper investigates the spatial importance of horses in a multifunctional and urbanized area. The growing spatial importance of horses in the open space was already mentioned by different authors, but never quantifi ed before. In many countries, including Belgium, statistics on horses are only partly covered by agricultural data. As a consequence, the amount of space in use for horses, especially hobby horses, is largely unknown but may encompass a signifi cant area of the open space. Especially within the context of an increasing urbanization and growing demands on the remaining rural area, this evolution must not be neglected. A reliable quantifi cation of the space used by horses is therefore essential and is given in this research for the case study Flanders. According to the results of fi eldwork, about one-third of the pasture land in Flanders is used to keep horses. A qualitative analysis showed a higher horse density within the more urbanized areas with a fragmented agricultural area and a quantitative analysis showed negative associations between the presence of horses and (i) the distance to gardens, (ii) the parcel area and (iii) the distance to forest. Moreover, an internet survey assessed evolutions and motivations of horse owners to keep horses. The survey resulted in clear data on the fact that the number of horses is increasing. This is mainly motivated by recreational purposes. The majority of horsekeepers do not consider themselves to be part of the agricultural sector. These results, showing an intensifi ed competition for land between stakeholders in the open space of urbanized regions put new challenges for sustainable land use planning. The major challenges are (i) to avoid increasing functional and spatial fragmentation of rural landscapes, (ii) to assure enough space for societal necessity urgencies such as food or energy selfeffi ciency, (iii) to increase positive interactions of horse keeping with other sectors such as agriculture, nature conservation and others and (iv) to develop a proper visual and cultural landscape strategy, helping in setting up guidelines for fencing and other infrastructural elements that do not deteriorate the landscape character.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".